TSF model paper sol.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tDpRBgr392kE"
   },
   "source": [
    "# Time Series - 1 Graded assessment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wb4GhWle92kL"
   },
   "source": [
    "### DATA_SET:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analytics firm wants to forecast the Price of Mindtree Ltd. stock for the month of Dec 2021. For this, firm has gathered a Closing Stock Price data for the period of Dec 2020 to Nov 2021."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2LED0-S192kT"
   },
   "source": [
    "* date ==\t\t\tdate field\t\n",
    "* Closing == \t    Daily Closing Price of Stock (numeric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wVAVAxL_92k5"
   },
   "source": [
    "#### 1.\tData preparation (5 marks)\n",
    "\n",
    "\n",
    "a.\tRead the dataset (tab, csv, xls, txt, inbuilt dataset). What are the number of rows and no. of cols & types of variables? (1 MARK)\n",
    "\n",
    "b. convert the data into time series (2 MARK)\n",
    "\n",
    "\n",
    "c. Check for defects in the data such as missing values, null, etc. (1 MARK)\n",
    "\n",
    "d. Visualize the time series using relevant plots. (1 MARK)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "G4pGllot92k8"
   },
   "outputs": [],
   "source": [
    "import pandas                             as      pd\n",
    "import numpy                              as      np\n",
    "import matplotlib.pyplot                  as      plt\n",
    "import seaborn                            as      sns\n",
    "from   pylab                              import  rcParams \n",
    "from   datetime                           import  datetime, timedelta\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"data_set.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Avg spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-02-04</th>\n",
       "      <td>04-02-2019</td>\n",
       "      <td>62.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-05</th>\n",
       "      <td>05-02-2019</td>\n",
       "      <td>64.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-06</th>\n",
       "      <td>06-02-2019</td>\n",
       "      <td>63.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-07</th>\n",
       "      <td>07-02-2019</td>\n",
       "      <td>61.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-08</th>\n",
       "      <td>08-02-2019</td>\n",
       "      <td>61.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                  Date  Avg spending\n",
       "timestamp                           \n",
       "2019-02-04  04-02-2019         62.58\n",
       "2019-02-05  05-02-2019         64.27\n",
       "2019-02-06  06-02-2019         63.44\n",
       "2019-02-07  07-02-2019         61.50\n",
       "2019-02-08  08-02-2019         61.16"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date = pd.date_range(start='02/04/2019', end='09/30/2020')\n",
    "df['timestamp']=date\n",
    "df.set_index('timestamp',inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Avg spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-02-04</th>\n",
       "      <td>04-02-2019</td>\n",
       "      <td>62.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-05</th>\n",
       "      <td>05-02-2019</td>\n",
       "      <td>64.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-06</th>\n",
       "      <td>06-02-2019</td>\n",
       "      <td>63.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-07</th>\n",
       "      <td>07-02-2019</td>\n",
       "      <td>61.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-08</th>\n",
       "      <td>08-02-2019</td>\n",
       "      <td>61.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date  Avg spending\n",
       "timestamp                           \n",
       "2019-02-04  04-02-2019         62.58\n",
       "2019-02-05  05-02-2019         64.27\n",
       "2019-02-06  06-02-2019         63.44\n",
       "2019-02-07  07-02-2019         61.50\n",
       "2019-02-08  08-02-2019         61.16"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Avg spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-09-26</th>\n",
       "      <td>26-09-2020</td>\n",
       "      <td>623.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-27</th>\n",
       "      <td>27-09-2020</td>\n",
       "      <td>656.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-28</th>\n",
       "      <td>28-09-2020</td>\n",
       "      <td>679.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-29</th>\n",
       "      <td>29-09-2020</td>\n",
       "      <td>671.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>30-09-2020</td>\n",
       "      <td>688.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date  Avg spending\n",
       "timestamp                           \n",
       "2020-09-26  26-09-2020        623.71\n",
       "2020-09-27  27-09-2020        656.57\n",
       "2020-09-28  28-09-2020        679.82\n",
       "2020-09-29  29-09-2020        671.87\n",
       "2020-09-30  30-09-2020        688.72"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 605 entries, 2019-02-04 to 2020-09-30\n",
      "Data columns (total 2 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Date          605 non-null    object \n",
      " 1   Avg spending  605 non-null    float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 14.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 605 entries, 2019-02-04 to 2020-09-30\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   Date          605 non-null    object        \n",
      " 1   Avg spending  605 non-null    float64       \n",
      " 2   date          605 non-null    datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(1), object(1)\n",
      "memory usage: 18.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Date', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg spending</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-02-04</th>\n",
       "      <td>62.58</td>\n",
       "      <td>2019-04-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-05</th>\n",
       "      <td>64.27</td>\n",
       "      <td>2019-05-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-06</th>\n",
       "      <td>63.44</td>\n",
       "      <td>2019-06-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-07</th>\n",
       "      <td>61.50</td>\n",
       "      <td>2019-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-08</th>\n",
       "      <td>61.16</td>\n",
       "      <td>2019-08-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Avg spending       date\n",
       "timestamp                          \n",
       "2019-02-04         62.58 2019-04-02\n",
       "2019-02-05         64.27 2019-05-02\n",
       "2019-02-06         63.44 2019-06-02\n",
       "2019-02-07         61.50 2019-07-02\n",
       "2019-02-08         61.16 2019-08-02"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('date', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Avg spending    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timestamp'>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15,8\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Evqx7ksA92lT"
   },
   "source": [
    "#### 2.\tData understanding (15 marks)\n",
    "\n",
    "a.\tDecompose the time series and check for time series components. (4 marks)\n",
    "\n",
    "b.\tPerform dicky fuller test to check the stationarity? What other actions will you take if series is non-stationary?(3+1 marks)\n",
    "\n",
    "c.\tPlot AutoCorrelation and Partial AutoCorrelation function? What is your inference from these plots? (2+2 marks)\n",
    "\n",
    "d.\tSplit dataset into train and test sets. Use last two month data for testing. (3 marks)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "### a. Decompose the time series and check for time series components. (4 marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-02-04</th>\n",
       "      <td>62.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-05</th>\n",
       "      <td>64.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-06</th>\n",
       "      <td>63.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-07</th>\n",
       "      <td>61.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-08</th>\n",
       "      <td>61.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Avg spending\n",
       "timestamp               \n",
       "2019-02-04         62.58\n",
       "2019-02-05         64.27\n",
       "2019-02-06         63.44\n",
       "2019-02-07         61.50\n",
       "2019-02-08         61.16"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seas_mul=seasonal_decompose(df, model='mul')\n",
    "seas_mul.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp\n",
       "2019-02-04           NaN\n",
       "2019-02-05           NaN\n",
       "2019-02-06           NaN\n",
       "2019-02-07     62.554286\n",
       "2019-02-08     62.418571\n",
       "                 ...    \n",
       "2020-09-26    641.815714\n",
       "2020-09-27    652.118571\n",
       "2020-09-28           NaN\n",
       "2020-09-29           NaN\n",
       "2020-09-30           NaN\n",
       "Name: trend, Length: 605, dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seas_mul.trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp\n",
       "2019-02-04    0.997207\n",
       "2019-02-05    0.995287\n",
       "2019-02-06    0.996032\n",
       "2019-02-07    1.001660\n",
       "2019-02-08    1.006429\n",
       "                ...   \n",
       "2020-09-26    0.998849\n",
       "2020-09-27    1.004536\n",
       "2020-09-28    0.997207\n",
       "2020-09-29    0.995287\n",
       "2020-09-30    0.996032\n",
       "Name: seasonal, Length: 605, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seas_mul.seasonal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "### b. Perform dicky fuller test to check the stationarity? What other actions will you take if series is non-stationary?(3+1 marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.30908265623618264,\n",
       " 0.9242080692970704,\n",
       " 9,\n",
       " 595,\n",
       " {'1%': -3.441388211993052,\n",
       "  '5%': -2.8664097967491498,\n",
       "  '10%': -2.569363480686392},\n",
       " 4908.380183800914)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools            import  adfuller\n",
    "observations= df.values\n",
    "test_result = adfuller(observations)\n",
    "test_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "####series is non-stationary. applying 1st order differencing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-6.731902702457797,\n",
       " 3.279870523197202e-09,\n",
       " 8,\n",
       " 595,\n",
       " {'1%': -3.441388211993052,\n",
       "  '5%': -2.8664097967491498,\n",
       "  '10%': -2.569363480686392},\n",
       " 4899.107905068978)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_diff = df.diff(periods=1).dropna()\n",
    "observations= df_diff.values\n",
    "test_result = adfuller(observations)\n",
    "test_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "### c. Plot AutoCorrelation and Partial AutoCorrelation function? What is your inference from these plots? (2+2 marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import  plot_acf,plot_pacf\n",
    "plot_acf(df,lags=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pacf(df,lags=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### Original series is showing AR characteristics as ACF plot is decaying slowly, while PACF is showing limited number of spikes before cut-off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "### d. Split dataset into train and test sets. Use last two month data for testing. (3 marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test= train_test_split(df, test_size=0.1, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-02-04</th>\n",
       "      <td>62.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-05</th>\n",
       "      <td>64.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-06</th>\n",
       "      <td>63.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-07</th>\n",
       "      <td>61.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-08</th>\n",
       "      <td>61.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Avg spending\n",
       "timestamp               \n",
       "2019-02-04         62.58\n",
       "2019-02-05         64.27\n",
       "2019-02-06         63.44\n",
       "2019-02-07         61.50\n",
       "2019-02-08         61.16"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-27</th>\n",
       "      <td>640.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-28</th>\n",
       "      <td>618.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-29</th>\n",
       "      <td>611.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-30</th>\n",
       "      <td>635.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-31</th>\n",
       "      <td>667.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Avg spending\n",
       "timestamp               \n",
       "2020-07-27        640.39\n",
       "2020-07-28        618.71\n",
       "2020-07-29        611.29\n",
       "2020-07-30        635.62\n",
       "2020-07-31        667.93"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-08-01</th>\n",
       "      <td>661.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-02</th>\n",
       "      <td>691.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-03</th>\n",
       "      <td>691.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-04</th>\n",
       "      <td>670.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-05</th>\n",
       "      <td>683.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Avg spending\n",
       "timestamp               \n",
       "2020-08-01        661.75\n",
       "2020-08-02        691.05\n",
       "2020-08-03        691.62\n",
       "2020-08-04        670.97\n",
       "2020-08-05        683.80"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-09-26</th>\n",
       "      <td>623.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-27</th>\n",
       "      <td>656.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-28</th>\n",
       "      <td>679.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-29</th>\n",
       "      <td>671.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>688.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Avg spending\n",
       "timestamp               \n",
       "2020-09-26        623.71\n",
       "2020-09-27        656.57\n",
       "2020-09-28        679.82\n",
       "2020-09-29        671.87\n",
       "2020-09-30        688.72"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'module' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-96-af6b68d64d8b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain_end\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2020\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mtest_end\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2020\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mtrain\u001b[0m             \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mtrain_end\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mtest\u001b[0m              \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtrain_end\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mtimedelta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdays\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mtest_end\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'module' object is not callable"
     ]
    }
   ],
   "source": [
    "train_end=datetime(2020,7,30)\n",
    "test_end=datetime(2020,9,1)\n",
    "train             = df[:train_end] \n",
    "test              = df[train_end + timedelta(days=1):test_end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(544, 1)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61, 1)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hAVdTKBS92ls"
   },
   "source": [
    "\n",
    "### 3.\tModel Building (20 marks)\n",
    "\n",
    "a.\tFit a base model and observe the residuals, RMSE and MAPE values of the model. Please comment on whether it is good or not.  (5 marks)\n",
    "\n",
    "c.\tHow would you improve the model? What changes you will make in the base model. Fit the final model.   (10 marks)\n",
    "\n",
    "d.\tAnalyze the residuals of final model. Feel free to use charts or graphs to explain. (2 marks) \n",
    "\n",
    "e.\tForecast the Close price for next 1 months using the final model? (3 marks)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Base Model using ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "id": "pc6Zny9Y92lu"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parameter combinations for the Model\n",
      "Model: (0, 0, 1)\n",
      "Model: (0, 0, 2)\n",
      "Model: (0, 0, 3)\n",
      "Model: (0, 1, 0)\n",
      "Model: (0, 1, 1)\n",
      "Model: (0, 1, 2)\n",
      "Model: (0, 1, 3)\n",
      "Model: (1, 0, 0)\n",
      "Model: (1, 0, 1)\n",
      "Model: (1, 0, 2)\n",
      "Model: (1, 0, 3)\n",
      "Model: (1, 1, 0)\n",
      "Model: (1, 1, 1)\n",
      "Model: (1, 1, 2)\n",
      "Model: (1, 1, 3)\n",
      "Model: (2, 0, 0)\n",
      "Model: (2, 0, 1)\n",
      "Model: (2, 0, 2)\n",
      "Model: (2, 0, 3)\n",
      "Model: (2, 1, 0)\n",
      "Model: (2, 1, 1)\n",
      "Model: (2, 1, 2)\n",
      "Model: (2, 1, 3)\n",
      "Model: (3, 0, 0)\n",
      "Model: (3, 0, 1)\n",
      "Model: (3, 0, 2)\n",
      "Model: (3, 0, 3)\n",
      "Model: (3, 1, 0)\n",
      "Model: (3, 1, 1)\n",
      "Model: (3, 1, 2)\n",
      "Model: (3, 1, 3)\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import  ARIMA\n",
    "import itertools\n",
    "p = q = range(0, 4)\n",
    "d= range(0,2)\n",
    "pdq = list(itertools.product(p, d, q))\n",
    "print('parameter combinations for the Model')\n",
    "for i in range(1,len(pdq)):\n",
    "    print('Model: {}'.format(pdq[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param</th>\n",
       "      <th>AIC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [param, AIC]\n",
       "Index: []"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfObj1 = pd.DataFrame(columns=['param', 'AIC'])\n",
    "dfObj1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 0) - AIC:7528.666416392138\n",
      "ARIMA(0, 0, 1) - AIC:6833.0093880700515\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 2) - AIC:6260.184535145538\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 3) - AIC:5902.097654436934\n",
      "ARIMA(0, 1, 0) - AIC:4519.383478542759\n",
      "ARIMA(0, 1, 1) - AIC:4518.689274784898\n",
      "ARIMA(0, 1, 2) - AIC:4520.53424845236\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 1, 3) - AIC:4520.218807261403\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 0, 0) - AIC:4538.01067394217\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 1, 0) - AIC:4518.82214362352\n",
      "ARIMA(1, 1, 1) - AIC:4520.636294389815\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 1, 2) - AIC:4522.309555429481\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 1, 3) - AIC:4522.072983957709\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(2, 0, 0) - AIC:4537.874044354221\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(2, 1, 0) - AIC:4520.274159479382\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(2, 1, 1) - AIC:4521.994414499837\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(2, 1, 3) - AIC:4496.126347633697\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(3, 0, 0) - AIC:4539.517396253377\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(3, 1, 0) - AIC:4520.35747186249\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(3, 1, 1) - AIC:4521.879909433706\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(3, 1, 2) - AIC:4496.1253924536795\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(3, 1, 3) - AIC:4494.695753678148\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  warnings.warn('Inverting hessian failed, no bse or cov_params '\n"
     ]
    }
   ],
   "source": [
    "for param in pdq:\n",
    "            try:\n",
    "                mod = ARIMA(train, order=param)\n",
    "                results_Arima = mod.fit()\n",
    "                print('ARIMA{} - AIC:{}'.format(param, results_Arima.aic))\n",
    "                dfObj1 = dfObj1.append({'param':param, 'AIC': results_Arima.aic}, ignore_index=True)\n",
    "\n",
    "            except:\n",
    "                continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param</th>\n",
       "      <th>AIC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(3, 1, 3)</td>\n",
       "      <td>4494.695754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(3, 1, 2)</td>\n",
       "      <td>4496.125392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(2, 1, 3)</td>\n",
       "      <td>4496.126348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(0, 1, 1)</td>\n",
       "      <td>4518.689275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(1, 1, 0)</td>\n",
       "      <td>4518.822144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(0, 1, 0)</td>\n",
       "      <td>4519.383479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(0, 1, 3)</td>\n",
       "      <td>4520.218807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(2, 1, 0)</td>\n",
       "      <td>4520.274159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(3, 1, 0)</td>\n",
       "      <td>4520.357472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(0, 1, 2)</td>\n",
       "      <td>4520.534248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(1, 1, 1)</td>\n",
       "      <td>4520.636294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(3, 1, 1)</td>\n",
       "      <td>4521.879909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(2, 1, 1)</td>\n",
       "      <td>4521.994414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(1, 1, 3)</td>\n",
       "      <td>4522.072984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(1, 1, 2)</td>\n",
       "      <td>4522.309555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(2, 0, 0)</td>\n",
       "      <td>4537.874044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(1, 0, 0)</td>\n",
       "      <td>4538.010674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(3, 0, 0)</td>\n",
       "      <td>4539.517396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(0, 0, 3)</td>\n",
       "      <td>5902.097654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(0, 0, 2)</td>\n",
       "      <td>6260.184535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(0, 0, 1)</td>\n",
       "      <td>6833.009388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(0, 0, 0)</td>\n",
       "      <td>7528.666416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        param          AIC\n",
       "21  (3, 1, 3)  4494.695754\n",
       "20  (3, 1, 2)  4496.125392\n",
       "16  (2, 1, 3)  4496.126348\n",
       "5   (0, 1, 1)  4518.689275\n",
       "9   (1, 1, 0)  4518.822144\n",
       "4   (0, 1, 0)  4519.383479\n",
       "7   (0, 1, 3)  4520.218807\n",
       "14  (2, 1, 0)  4520.274159\n",
       "18  (3, 1, 0)  4520.357472\n",
       "6   (0, 1, 2)  4520.534248\n",
       "10  (1, 1, 1)  4520.636294\n",
       "19  (3, 1, 1)  4521.879909\n",
       "15  (2, 1, 1)  4521.994414\n",
       "12  (1, 1, 3)  4522.072984\n",
       "11  (1, 1, 2)  4522.309555\n",
       "13  (2, 0, 0)  4537.874044\n",
       "8   (1, 0, 0)  4538.010674\n",
       "17  (3, 0, 0)  4539.517396\n",
       "3   (0, 0, 3)  5902.097654\n",
       "2   (0, 0, 2)  6260.184535\n",
       "1   (0, 0, 1)  6833.009388\n",
       "0   (0, 0, 0)  7528.666416"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfObj1.sort_values(by=['AIC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:           Avg spending   No. Observations:                  544\n",
      "Model:                 ARIMA(3, 1, 3)   Log Likelihood               -2241.157\n",
      "Date:                Sun, 01 Sep 2024   AIC                           4496.314\n",
      "Time:                        12:05:44   BIC                           4526.394\n",
      "Sample:                    02-04-2019   HQIC                          4508.075\n",
      "                         - 07-31-2020                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1         -0.8238      0.024    -34.451      0.000      -0.871      -0.777\n",
      "ar.L2         -0.7866      0.013    -58.594      0.000      -0.813      -0.760\n",
      "ar.L3         -0.9482      0.020    -47.489      0.000      -0.987      -0.909\n",
      "ma.L1          0.7456      0.028     26.173      0.000       0.690       0.801\n",
      "ma.L2          0.7635      0.014     55.474      0.000       0.737       0.791\n",
      "ma.L3          0.9767      0.026     37.783      0.000       0.926       1.027\n",
      "sigma2       219.2899      6.350     34.536      0.000     206.845     231.735\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.02   Jarque-Bera (JB):              2014.55\n",
      "Prob(Q):                              0.89   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):             235.10   Skew:                             0.31\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                        12.42\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "model = sm.tsa.arima.ARIMA(train, order=(3,1,3))\n",
    "results_Arima = model.fit()\n",
    "print(results_Arima.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "ARIMA_predictions=results_Arima.forecast(len(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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itY0bN2IYBs8888yIj19EREROro5wBEe8albvDZIXX6s2tTCDg83t7G3wkZPmID/DSW66s1dgg3hoi69pa/aFEhtvHy0nfq5XlTZJMQptx5Cens6mTZvYsmULLpeLe++9t8f70Wi0nzOP7Re/+AXz58/v9/3hhrZjefHFF9m8eTNvvvkm+/bt45Of/OQxjz9Roe3BBx9kxYoVPPLIIz1eP+uss9i4cSMbN27kiSee4NVXX+3z/EWLFvHQQw8lfn744Yd73csHH3yQVatW8eCDD474+EVEROTkag9FEyErGjMT686mFmUSisRYv7eJacVZGIbR7zWKs9w0eAJEYybeYITc9N7BDroqdFrTJqlGoW2QzjrrLPbs2cPatWtZs2YNN910E4sWLSIajfKFL3yBFStWsHjxYn72s58BYJomn/70p5k/fz6XXXYZ9fVdbWlXr16dqAw9/fTTLFu2jCVLlnDeeedx4MAB7r33Xn7wgx9w5pln8vLLL9PQ0MB1113HihUrWLFiRSLQNDU1ceGFF7J06VJuueWWQbWnzcrK4t577+XRRx+lubkZn8/Heeedx7Jly1i0aBGPPfYYAF/60pfYu3cvlZWVfOELX+j3uKHYu3cvPp+Pb37zm/0GqvT0dCorKzly5Eif71999dWJz963bx+5ubkUFxcn3jdNk4cffpjf/OY3PPvsswQCgSGPU0RERFJHezDaozLWGbimFWYCsK/Rz7TCvqc7duqstHnjYayzScnRctKt11Vpk1QzKta0fffN77KjeceIXnNuwVy+uPKLgzo2Eonw1FNPcfHFFwPw5ptvsmXLFqZNm8Z9991Hbm4ub731FsFgkDPPPJMLL7yQjRs3snPnTjZv3kxdXR3z58/nox/9aI/rNjQ08IlPfIJ169Yxbdo0mpubKSgo4NZbbyUrK4tbbrmF7OxsbrrpJu644w5WrVrFoUOHuOiii9i+fTvf+MY3WLVqFf/5n//J3//+d+67775BfZ+cnBymTZvG7t27OeWUU3jkkUfIycmhsbGR0047jSuvvJLvfOc7bNmyhU2bNiXuQV/HHetvtY724IMPcuONN3LWWWexc+dO6uvrKSkp6XFMS0sLu3fv5uyzz+537JMmTWLLli089thjXH/99fz6179OvL9+/XqmTZvGjBkzWL16NU8++STXXnvtoMcoIiIiqaU9HKEwqyu0JaZHFmUmXptWlHXMaxRkOglHTWo91l/m5vQxhRIg3WnHbjO0pk1Sjiptx9DR0UFlZSXLly9n8uTJfOxjHwNg5cqVTJs2DYBnn32W3/72t1RWVnLqqafS1NTE7t27WbduHTfeeCN2u50JEyZw7rnn9rr++vXrOfvssxPXKigo6HMczz33HJ/+9KeprKzkyiuvxOPx4PV6WbduHR/60IcAuOyyy8jPzx/0d+usypmmyVe+8hUWL17M+eefz5EjR6irq+vz+MEcdywPPfQQN9xwAzabjWuvvZY///nPifdefvllFi9eTFlZGZdffjllZWX9XueGG27goYce4tFHH+Waa67p8d6f//xnbrjhhsRxmiIpIiIyurWHouSlu4gvayMvXmkry0nD7bB+lZ1WnNnf6UBXSOvcMqC/SpthGOSkOVRpk5QzKiptg62IjbTONW1Hy8zs+heDaZr8+Mc/5qKLLupxzJNPPjlgFco0zUFVqmKxGK+//jrp6em93htKpauT1+vlwIEDzJ49mwceeICGhgY2bNiA0+lk6tSpfU4pHMxxjzzyCN/4xjcAa93e8uXLE++999577N69mwsuuACAUCjE9OnTuf322wFr+ukTTzzBrl27WLVqFddccw2VlZV9jv+KK67gC1/4AsuXLycnJyfxejQa5fHHH+fpp5/mW9/6FqZp0tTUhNfrJTs7e8j3SURERJKvIxQlw2Un3WnHH4om1rTZbAZTCjPYVedLTJXsT+eUyqpEaOu70tb5nldr2iTFqNJ2nC666CL+7//+j3DY+od7165d+P1+zj77bB566CGi0Sg1NTW8+OKLvc49/fTTeemll9i/fz8Azc3NAGRnZ+P1ehPHXXjhhdxzzz2JnzuD5Nlnn80DDzwAwFNPPUVLS8uA4/X5fHzqU5/i6quvJj8/n7a2NkpKSnA6nbz44oscPHiwzzH0d1x311xzDZs2bWLTpk09AhtYUyPvvPNODhw4wIEDB6iurubIkSO9rjN79my+/OUv893vfrff75Cens53v/tdvvrVr/Z4/bnnnmPhwoUcPnyYAwcOcPDgQa677joeffTRAe+LiIiIpKb2ztDmsgP0aCIyNR7WphYde01b5/5rR1qPXWmzjnVonzZJOQptx+njH/848+fPZ9myZSxcuJBbbrmFSCTCNddcw6xZs1i0aBG33XYb55xzTq9zi4uLue+++7j22mtZsmQJ119/PWBVkh555JFEI5If/ehHvP322yxevJj58+cnulh+/etfZ926dSxbtoxnn32WyZMn9zvONWvWsHDhQlauXMnkyZMTDVM++MEP8vbbb7N8+XIeeOAB5s6dC0BhYSFnnnkmCxcu5Atf+EK/xw3WQw891Gsq4zXXXNOjE2SnW2+9lXXr1iXCbF9uuOEGli1b1uO1Bx98kCuuuKLHa9dddx1/+MMfhjRWERERSR0doSjpLgdpTiu0da5pAzhrVhGnTis4ZuUMuoLeQNMjAbLdqrRJ6jEG03HwRFu+fLnZfZ8tgO3btzNv3rwkjSg1aFrf0A32nunPV5e1a9eyevXqZA9j1NF9Gzrds6HR/Roa3a+hGw33LByNMeurT/G5C2bzt3er2V3v4xf/tJzz55cO6TqHmto5+79fZElFLu9WtfHWV8+nONvd57G3/O5tDjS288wdvZuijYZ7lop03wbHMIwNpmku7+s9VdpEREREJCW1h6w9cbtPj+xc0zYUuUOYHqk1bZKKFNpEREREJCV1xENbusvebXrk0ENbVjykNfpCuOy2xLX6kpPm1Jo2STkKbSIiIiKSktpDVnjq7B4JkJvuOtYpfbLbjER17VhVts73fcEI0ZjJH944xP97cvuQP09kpKV0aEuF9XYy9ujPlYiIyOjQNT3S0S20Db3SBl17tQ0U2jo7TW6tbuPOv23ld+sP6ncHSbqUDW1paWk0NTXpHxIZUZ17t6WlpSV7KCIiIjKAjnDPNW2ZLjsux/B+fe0MewN1muwMdf/+8HuEIjHaQ1EafaFhfabISEnZzbUrKiqoqqqioaEh2UNJmkAgoHAxRIO5Z2lpaVRUVJykEYmIiMhwdW9EsqQiF39w+GvNctIHNz2ysyK3o9bL0sl5bDzUyqFmf7/dJkVOhpQNbU6nk2nTpiV7GEm1du1ali5dmuxhjCq6ZyIiImNHezykpTsd3HzmNG4+c/i/G3ZV2gYKbV3vf+HCOdz0izc41NzOKVMKhv3ZIsdrUPVlwzDuMAxjq2EYWwzDeNAwjDTDMAoMw/iHYRi744/53Y7/smEYewzD2GkYxkUnbvgiIiIiMlZ1r7Qdr641bceeHpnhtkKbYcCyKfkYBhxsaj/uzxc5HgOGNsMwJgKfBZabprkQsAM3AF8CnjdNcxbwfPxnDMOYH39/AXAx8FPDMI7/nzQRERERGVfawyMY2gZZaZtWlElFfjq/+eeVpDntlOWkcUihTZJssCs5HUC6YRgOIAOoBq4C7o+/fz9wdfz5VcBDpmkGTdPcD+wBVo7YiEVERERkXOjobPnvPv4VPYNtRJKb7uSVL57LObOLAZhckMHBZoU2SS5jMN0ZDcP4F+BbQAfwrGmaHzQMo9U0zbxux7SYpplvGMY9wHrTNH8ff/2XwFOmaT581DU/CXwSoLS09JSHHnpopL7TmOHz+cjKykr2MEYV3bOh0z0bHt23odM9Gxrdr6HR/Rq60XDPHt0T4tE9YX51UQY2wziua/3jYJgHtoe4YY6Li6cNftuAX24O8m5DhI8scDMtLUBBbmrfs1Q0Gv6spYI1a9ZsME1zeV/vDfjXFvG1alcB04BW4M+GYXzoWKf08VqvZGia5n3AfQDLly83V69ePdBQxp21a9ei+zI0umdDp3s2PLpvQ6d7NjS6X0Oj+zV0o+Gevd6+HfeBA5y7Zs1xX6tlYxUPbH+XpQvnsHrF5EGft9Xcw8vP7OTHG4NcP8fNd69afdxjGW9Gw5+1VDeY6ZHnA/tN02wwTTMM/BU4A6gzDKMcIP5YHz++CpjU7fwKrOmUIiIiIiKD1h6Kjsh6Nhh8I5KjnTGjkGlFmRRmutjeHB2RsYgM1WBC2yHgNMMwMgzDMIDzgO3A48BH4sd8BHgs/vxx4AbDMNyGYUwDZgFvjuywRURERGSs84ciZLhGZoeqSQUZAEzMSx/SeUsn5/Pi51dz8cIydjVHiURjIzIekaEYMLSZpvkG8DDwDrA5fs59wHeACwzD2A1cEP8Z0zS3An8CtgFPA7ebpqm/lhARERGRIekIRUkfoUrb7NJsXv/yuSyZlDes80+bXkggClurPSMyHpGhGNRfXZim+XXg60e9HMSquvV1/LewGpeIiIiIiAzLSE6PBCjPHVqVrbtTp1uba6/f1zTs4CcyXINt+S8iIiIiclJ1jHBoOx4l2WlUZBk8ubmGwXRfFxlJCm0iIiIikpLawyO3pm0knDvZybtVbWw42JLsocg4o9AmIiIiIimpfQTXtI2EMyc4yE138ouX9yd7KDLOKLSJiIiISErqCEXJcKZOaHM7DC5eUMYb+5uSPRQZZxTaRERERCQl+YORlFnT1ik3w0l7SI3R5eRSaBMRERGRlNQRjpKeQmvaANKddoKRGLGYmpHIyaPQJiIiIiIpJxyNEY6aZKZYpa1zjV0gomqbnDwKbSIiIiKScjqnIKZSIxKwKm2ApkjKSaXQJiIiIiIppyMeilKp5T90hcgOhTY5iRTaRERERCTltIciACnXiKSz0hYIK7TJyaPQJiIiIiIpR9MjRbootImIiIhIymlPTI9MrdDWOZ6O0VRpC/rgb/8KgbZkj0SGSaFNRERERFJOqk6PTBuNoe3I27Dh13Dg1WSPRIZJoU1EREREUk7KNiJxjsJGJCG/9eivT+44ZNgU2kREREQk5aT89MhRFdrarUdfQ3LHIcOm0CYiIiIiKafBFwQgP9OV5JH0lKi0jabpkSGf9ahK26il0CYiIiIiKedwczt5GU5y0pzJHkoPaaOx0hburLQptI1WCm0iIiIiknIOt3QwKT8j2cPoZXRW2jrXtGl65Gil0CYiIiIiKaequZ1JBenJHkYvTrsNp90YnaFNlbZRS6FNRERERFJKLGZSlaKVNoA0p310TY8cqHtk0AcdrSdtODJ0Cm0iIiIiklLqvUFC0RgVBakZ2jJcXaGttT2ENxBO8ogG0LmmLdAGkWDv95/8PPzh+pM7JhkShTYRERERSSmHW6yQMTlFQ1u60057fHrkLb/bwJ2Pb0vyiAbQWWkDa11bJNiz/X/DDut/krJSa7dCERERERn3DjdboW1SfuqtaYOe0yOrWjpw2I0kj2gA3UPb2u/AtsfANOELu8GZDp4aCLRax7ky4cCrkFsB+VOSNmTpSZU2EREREUkph5s7MAyYmKKhLcNlJxCvtHkC4cRG4Ckr3A6O+L3c+DvAgJAXPNUQDYOvznrPU21txP376+Dl7yVtuNKbQpuIiIiIpJSddR5Ks9NwO+zJHkqf0l122kMRYjETXzBCezDFQ1vI17NqduZnrEdPdTywmfGfj8C+FyHSoU6TKUahTURERERSxv5GP89srePSReXJHkq/0p12OsIxvMEIpgnt4Uiyh3RsoXbIn2o9L10I86+2nnuqrf918lTDziet5/7GkzlCGYDWtImIiIhIyvjxC7tx2g1uWz0j2UPpV7rLQSAcxdNhdY1M/UqbHzKLYMmNsOAayI4HYm81ONxdx7Uehp1PW8/bm07+OKVfCm0iIiIikjL+sbWOq5ZMpDjbPfDBSZLutNEeitDWGdpSfk2bH5yZcOndXa+5c63Kmj1+n+1u2P43aG+ErDKFthSj6ZEiIiIikhK8gTDeYITpxZnJHsoxpce7R3ri+7N1hKPEYmaSR3UMnV0hu8uZYIU2bzy4Fc+Gus1g2GDhtRD09L2nmySFQpuIiIiIpITatgAA5Xmp2TWykzU9Moano2stW0c4RattkRDEIuA6as+7nPKuNW05EyCnwnp9wjIonGk9b28+uWOVfim0iYiIiEhKqI6Htgm5aUkeybGlO+2EojFa2kOJ1/yhFG1GEo7v0ebK6vl6zgTw1lh7tOVMhNyJ1uszz7PWv4E1VVJSgkKbiIiIiKSEmtYOAMpSPLRluKytCOo8gcRrnc1I6j0BwtFYUsbVp86NtZ1HVdqyJ1jt/lsPWVW3nAnW6zPOg4x4aFMHyZSh0CYiIiIiKaGmLYBhQGlOaoe2tERo61rz1R6KEoxEOff7L/HQW4eTNbTeQu3WY19r2swYeKqgZL7VVXLV56BiOWQUWseoGUnKUPdIEREREUkJNW0dlGS7cdpTu66Q7uyj0haK0NYexheMcLDRn6yh9RbyWY99hTaA3Elw6i3W++d/3XotMT1SoS1VpPY/ESIiIiIybtS0BSjLTe0mJAA5aVbd42BTVzhr79ZNsskf6vO8pAjHK21HT48smQ9peXD5D3oHuvR8wND0yBSiSpuIiIiIpISatgCzSrIGPjDJZpVmA7C3wY/dZhCNmfF926xmJCkV2kL9NCLJmwRfPACG0fscm90Kbqq0pQxV2kREREQk6UzTpKa1I+WbkABMLsjA7bB+jS7Osjan7l5pa/YPfn+zQDjKliNtIz/ITonQltH7vb4CW6eMwp7dIx94P6z9zsiOTQZNoU1EREREks4TiOAPRZkwCqZH2m0Gs0qtylVnyPSHong64tMjfYOvtP1+/UGu+smrtMXPHXGJ0DbEDcszi3ru03b4DTi0fuTGJUOi0CYiIiIiSde5PmxifuqHNoDZ8SmSZfFOlx2hCJ5A1/RI0zQHdZ1Nh1uJxkwavIGBDx6OxJq2IYa2jELwN1jPIyEItFkbcUtSKLSJiIiISNK9ud+q6pwyJT/JIxmcOfHQVppjTY/0B7sqbaFIDH8oOqjrbKv2ANDoC/Ha3kYON7eP7ECHW2krnguNuyHg6VrbptCWNAptIiIiIpJ06/c1M60oM+X3aOs0u8wKbXkZLtKddtpDkURoA2jyHXtd26t7Gtnb4GN/vMLY6Atyy+82cN+6fSM70I4WsLvA4R7aedPPATMKB1/tWtsW8loVNznp1D1SRERERJIqGjN5c38Tly4qT/ZQBm1eWQ4ARVkuMlx22kNRYt2mRDb5Q0wp7Lu6ZZomH7v/LfIzXHSecrCpHW8gQutIr23z1lh7sh2r6UhfKlaCIw32vQSzL+p63VMNabkjO0YZkCptIiIiInJCba/xHPP9HbUePIEIp00vPEkjOn5luWk8+InTuHZZBRluK7R54i3/4djNSDyBCIFwjJq2rnVsnR0kfYERDm2easiZOPTznGkw+TTY/1LP/do8R0ZubDJoCm0iIiIicsK8e7iVS374Mu8caun3mHcOtQKwYlrBSRrVyDh9RiGZbgcZToc1PTIQpiTbmoZ4rLb/3adO5mc4Kcx0saU6HtqCkf5OGx7PEcgeZgVz2jlQvw0ad3a7nta1JYNCm4iIiIicMDVtHQDUtfXfHbG2rQO7zaB8lKxnO1pXpS3M1CJrSuSxNtjufO/iBWV84uzpFGW5Odxs3SdvYJih7eBr8MQd8Nw3ul4zTfDEp0cOR/kS6/HAK2DYAEOhLUm0pk1ERERETpjWdmu637HCSIM3SFGWC5ttiOuuUkSmyxHfXDvCwsJMMlz2Y06P7Ky0fea8mSyYkMurexrZWWe9N+zQ9uS/Q91m6/k5/w7OdGuftWhweNMjAYpmW49HNlhbABi20T89sm4r1G+HuZdbU0BHCVXaREREROSE6dw02nOMtVoN3iDF2UPsbphC0l12/EGre2ROmoOCTBfNx6i0NcYDXVGW9Z0LM7u++7CnR3prwGV1tKT1kPXYGbByhjk9MmciONIhGoLMYqtiN9orbdseg798HBjcPnqpQqFNRERERE6Y1kRoO0alzRekOGv0hrbMePdITyBMTrqTwiz3sadHxkNbfoYLgMIsV+I9XzAy6I25E6IRay+1SSusn1sOQCzWLbQNs9Jms0HRTOt5RqF1nbZRXmlr2An5U61K5Cii0CYiIiIiJ0zX9MixXGlz0OwPEY6a5KRZjUWOtU9bsz9IbroTl8P6VbyoW2CNxkwC4djQBtDeCJhWm36Axl3wP3PhxW9ZPw93TRt0TZHMLIKC6dC8F8L9r09MeY27oHhOskcxZAptIiIiInLCdG443d9arVjMpNEXGtWhrTDTlZjWmJtuhbZjTo/0hyjM7KqudT53xNf0HSvg9skXXxBXusCazrjj79ZrtZutdWiZJUO7XneJ0FZsbQEQDUH1O8O/XrJ466yw2bSn6zuNIgptIiIiInLCtHZY4aW/INLSHiIaM0f19MjLl3StGctJd1CQ5aLJF+p3mmOTL9hjSmRnpW1avPOkd6jr2nwN1mN2GeRPgUOvd72XVQb24+g9WNg5PbIIJp9uPT/42vCvlwyxKPxkBTx6mxU6VWkTEREREenSOT2y+8bT3TXEpxEWZ4+eTn5Hm1uWk3ieHZ8eGYrG+m0q0uQL9Wg+0hngZpVmAeAbagfJzkpbZjHkTbGeZxRBev7xTY2EntMjMwqgeF7PUDgaeGsh0AZb/2r9XKTQJiIiIiKS0Nk90hvsu9LW4O0MbaO30gZwx/lWuCnIcCUC2dFTJDtCUfbUe2nyh3pU2hZOzOW21TO4utJqGDLkDpKdoS2rxGqyATBpJVxzH5z7taF/me5KF8IFd8H8q6yfp5wOh9+0qlejRVtVz5+LNT1SRERERCShbYB92sZKaPvseTN58rNnsagil4J4IGs8aq+2Hz6/m/P/Zx3N/hCF3aaDOu02vnjxXCryM4BhrGnzN4ArC1yZ1vRIgInLYPaFMGPN8L8UWB0kz/ysVWkDmL4agh547uvW5t17X4QXv318n3GiebqFtqwySMtN3liGSaFNRERERE6ISDSWWJ/V2ZDkaGMltBmGwfwJ1jTJzsYiR1fafN2qjUXdKm2dstOstWfeQIRINMb3n91JnWcQnRp9dVaVDbrWoE1c3uOQaB+VsbZgW6/Xav21vdbi1bfXd70270pY8Ql8r98Dm/4A674Hr/wvADFziF0vT5bOStuiD8Dsi5I7lmE6jlWJIiIiIiL969ybLcNlxxuw9h8zDKPHMQ3eIOlOO5kuezKGeEJ0VtGa/T3b/menObuOyewdUrPc1q/mvmCE94608eMX9hCKxPjypfP6/Jx3D7dy70t7+UFHDV/NdhB78Q4unXox9y86i/9XPINXdzyEN+SlPKucb6//Nj8690cAeEIe2iPtfOXlr/Cds77DwqKFeEIeav213LH2Dr608ktcNPUi2oJtNHY08olnP8GnKj/FB+Z8gKaOJmKnfZwPNjzDP7/9P3yg7iA1DoOS1gPc9OxHuX7O9Vw2/TL2te1jaclSPvXcp5gemc7ElolsatjE1TOu5huvf4MVZSuYnjudl4+8zCcWf4Jfbf4Vs/JnkevO5cl9T/Jvy/+N5w89T1lmGQB/2vknvnLqV9jWtI08dx6hWIhfb/k1Xzn1K9T6a0l3pBMxI9y76V6+sOIL+MN+bIYNp83Jjw/9jc9k5hNe83miZpT5x/9/8Umn0CYiIiIiJ0Rru1VpmpSfwc46L4FwjPRu4SwWM9lZ56U4290rzI1mnZW2o6dHhiNWJeryxeWsmJrf67yseKXNF4iwo8YLwBPv1fDFi+dis/W+P/safTy1pZabK+p41hWGQ8/x3KHnAPjP177OxvqNRM2uCtvdb93NYe9hfGEfdsOOicmPNv6IYDRIU0cT6Q5rw+n/e/f/uH/r/TS0N5DrzsXE5Bebf8HDux6mqaOJsswyggbc5wzypwkltNhszHzxszR0NHDPpnv49dZf4w/7mV84n21N29jEJv7+978TjAb5666/sqVpC4/tfQynzUk4FmZd1Tq2Nm3FwMBhcxCOhdncuJntzdsTwSsYDbKndQ97W/diN+y47C7aI+3sa9tHlbcKm2Ejw5lBW7CNnS07aehowMAg151LY6iRjUU5tP39BgC+ccY3uHLGlcf1//HJpumRIiIiInJCtManRE4qsMKAp9tarbaOMLf/4R1e3t3IFd1a5o8FaU47GS57r+mR4WiMvAwn99y0jJKc3t0ynXYbaU4bvmCEHbUeAI60drDxcEufn9MUD4W/cbaTbzj5rzP+i2tnXcv1c67n7bq3cdvd/Muyf+H8yefzL8v+he3N2wnHwtw490bmFMzhztPv5IjvCL6Qj/Mmn4fdsHP32XfjCXrwhDxUllTSHGjm26u+jc2wEY6FWVC0gCpfFV9adCul0SgZsRhLgiF2e/Zz65JbWVi4kMnZk1lZtpJtTdu4fs71LExfyOLixayuWM2Wpi1cMvUSLpl6CctLl3PljCvZ2rSVsyaexaXTL2VpyVI+NO9DbG/ezoqyFVw45UJm5c3i9srb2dO6h8qSSs6qOIuSjBK+vPLLHPQcZHHxYlaWrcRhOLjz9DtpaG9gSfESlpUuoyPSwX+F0gnaDJaXLueUklO487U7qfHVjPD/6yeWKm0iIiIickJ0do7s3mCjNCeNtvYwl/34ZWraAnz10nl8/KxpyRzmCVGY1XuD7VDUxGk/ds0ky+3EE4iwt8HH3LJs9jf6eXJzLadMKeh1bJM/RIargdfcDj6du4BrZl3DNbOuoSXQwrMHnuXmhTfz0YUfjX92iPU167lk6iVcN/s6AEzT5KDnIEtLlrJm8hoisQgOmwPDMJiUPYm5+XOpb6+nPKuceQXzyHZlk+vOZXPjZpaXLueK6t24mvYRObKOdZfexQWLP8mti2/FZtjwhX08ue9Jrpx5JW8E3mD16tX4w34e2f0IV828imxXNgDBaJDFRYu5eNrF5LqtBiGRWIQFRQs4a+JZiddM02Rx0WIWFy8my5WVmGp7SukpTMmZgtvuJmJGcNqcnFVxFgVpBdgNO+2RdjL/ZyEXzb+C9At+SCQWYUvTFsqzRtdfFCi0iYiIiMgJ0dk5cnKBFdo617i9sb+JqpYO7vvwKVy4oCxp4zuRCjLdNPp6rmkLRWK4Bght2WkOvIEwO2o8XLHE2mPtUHN7n8c2+0KUufL5pXMWRdOvSryen5bPCx94AbvRNRXVZXfxiwt/0eN8wzD43PLPJX522KxocPHUixOvdYabmfkzE6+tKFsBQM7l/2u1///lBVyaNw9sXWv2sl3ZXD/3+h6fl+nM5EPzP9TjNbfd3es4h83B5dMv7zXWMyae0eNngDkFXXuuOQ3r80sySro+0wQ6msnImwKGgdPuZGnJUkYbhTYREREROSESa9oKOittVmjbVWet1zpjZlFyBnYSFGW6qD2q82M4GsNpP/bavew0B3vqfXgCkUSlremo8NepyR/EnV3AtA8+0uu9zgB2wnW2zw+0npzPG6q2I9Zj7qTkjuM4aU2biIiIiJwQbR1WSJuYF1/TFp8uubPOR0V+eqJb4lhUkOlKrDnrFI7GcDkGmh7pYEetFWrnludQmOXuNc2yU5M/lGh6kjSJ0NZ7+4CU4K22HnMmJHccx0mhTUREREROiGZ/kJw0BwXxYNFZadtd52VOaXYyh3bCFcTXtHXf88yqtA0c2sAKugsm5FDYR/jr1OwPJe5t0ritvelSNrT5G63HzOLkjuM4jd2/3hARERGRE8Y0TQ41tzOlMLPfY5r8IQqz3N02jQ4TjsbY2+Bj9ZySfs8bC4oy3YSiMXzBSGJ/tmBk4NB248rJTC/O4rZzZpDhclCU5cIbjBCMRHE7eu5l1+QLUdjHJt0nlTPdWssW9CR3HP3pDG0Zo3sqriptIiIiIjJkT7xXwzn/vZaDTf5+j+msBGW47LgcNmo9AQ40+glHTeaUZZ3E0Z58nRWw7lWycHTgRiRr5pbwpUvmkpvhjF+nc6PuntW2YCSKLxhJ/vRIw7CmSKZqpa29EQwbpPfeF280UWgTERERkSF7bJPV4KG2LdDvMc3+EPkZLgzDYMXUfF7d08jOeBOS2eNgeiRY1cZO4ag54Jq2o3VW0o6eItkZ4gqz3MczzJGRyqHN3wjpBWAb3bFndI9eRERERE46TyDMul3WtLPOdWp96d4oY/XsEnbV+XjwzUOkO+3MKB7blbaiPipkg+ke2es68dB29PYBnSEu6WvaILVDW3sjZI7uqZGg0CYiIiIiQ/TctjpC0RgA3mC4z2NM06TFH0pUnM6ZYzWCeHVPEzedOpk0p73P88aKRKWtW9gKDWJNW6/rxMPf0ZW2zgpe0qdHQmqHNn/TqF/PBgptIiIiIjJEbx9swWGzKkbdK20doSi/eHkfgXAUTyBCJGYmQsWskiwm5Kbhstv4xFnTkzLuk6nze3efHhkaRMv/XteJh7839zfz8fvfpiMUta4bD4OaHjmA9kbILEz2KI6bukeKiIiIyJC0dYQpy02jqqUjsfcawLrdDXzz79tJc9o5M75xduf0PcMw+OIlc/EHo5TlpiVl3CdTmtNOpss+5EYkR8t2O3DZbTz8ThXRmMn2Wg/LJucnpl1qeuQA/I2qtImIiIjI+OPpCFOc7cblsPWotDV4rerP79cfpNlvPe8eKq6qnMhNp04+uYNNImuvtq7pkeGIOeTpkYZhUJjlIhqz9nuraukArAqe026Qk5YCNZhUDW2xKHS0aE2biIiIiIw/nkCEnDQnOWkOPN1CW2ezjB21Xp7dVgdAYWYKTN9LksJM91HdI2M4HUNrRAI9g29VSzsA9Z4gRVluDGPo1xtxabkQCUC4/06iSdHeDJiqtImIiIjI+OPtCJOT7iQ7zYk30DU9stEXJNvtIM1p46E3DwNdDTnGo8JMV4/pkaFIDJd96A1Yuq9bOxKvtNV6OlJnmmlarvV4PBtsV2/q2gh7pLTHrzcG1rQptImIiIjIkLR1hMlJc5B9dKXNG6IsN43lUwpoi691K8gYv6GtINPVo+V/aJiVtrIcN7npTuaV5ySmR9a0BZiQmz5iYz0uaXnW43CnSEbD8JvL4ZFbR2xIQFcIVKVNRERERMYT0zTxBKxKW0680vaPbXW8vreJRl+Q4mw3p8+wKhvpTjvprrHd2v9YCrPcNPtDmKa1Hm04jUgAPnfBHB74+KlMLcygqqUd0zSpaQ2kTqUtu9R6bDk4vPNr34OQF/b8w6q4jZREpU2hTURERETGkUA4RjhqkpPmJDvNgTcQ4dtPbuf7z+6k0WetszojHtpSorNhEhVmughFY3iDEaIxk5jJkBuRAJTlprFwYi4V+elUtXTQ1hGmIxylPFVC24SlYNig6s3hnX/oDevRmQFPfREa94zMuFRpExEREZHxyBNfw5abboU2T0eYI60d7G3w0eC1QtuiiblkuR2JPcbGq87Q2uwLEYpYm5EPdZ+27iryMwhGYmw+Yk1DLE+V6ZHubChdAIfWD+/8w+shdzJc8l2oeRfuW211fRyOSAj++CHYcD8cet0KkxkFw7tWCkmBHqEiIiIiMlp07suWk+4gJ81JfbzNf2coKcp24bDb+PDpU0h3jt+pkdC1MXaTP0h+PMANp9LWqSLfCmlvHbACTcpMjwSYdBq8+yBEI2AfQsQwTavSNu0sWPZPkFEID90EDbtg8qlDH8fOv8P2v1n/Azjr38DuHPp1UoxCm4iIiIgMWmelzZoe2fuX4aJ4p8MvXjz3pI4rFZXmWKGqti3IlMJMAFz24bfor8jPAODN/U0AqTM9EmDSqfDWz6F+K5QvGfx5rQfBV2udD1A0x3ps3ju40NZ2BLJKu4Li27+G3Ekw7wpruuW5Xxva90hRmh4pIiIiIoPWlqi0WdMjj1acNX73ZTvahDyrMlbd2kE4alUij6fSNqUwgyy3gzf3N2MzoCQ7he71pJXWY9VbQzuv5l3rceIy6zF/Chh2aNo78LltVfCjStj0gPVz8z7Y/xIs+whc/P/gvP+AVNjHbgQotImIiIjIoHk6rBb/nS3/Ozls1i/HRQptCTlpDjJddqrbOkZkTVua087FC8uImVCSnYbjOALgiMubDHYXtB4a2nm1m62QVjLf+tnutK7VvG/gc7f8FaIhq/skwO7nrMfF7x/aGEaBFPp/WkRERERSXWJ6ZLqTnHRremS228Hs0mzAWtMmFsMwmJCXPmKVNoBrl04EUmw9G1gVraxS8NYN7bzazVA0G5zdmqoUTLemRw5ky8PWY2dV7vB6yC6HvClDG8MooNAmIiIiIoPW2Ygku1ulbUJeOjNKsgAozFSlrbvyvHRq2gKEItZebccb2k6bXsikgnSmF2eOxPBGVnaZtT5tKGq3QNnCnq8VzoCmfVaTkr7sfREe+qA1tdLm7Apth96w1saNkSmR3akRiYiIiIgMmicQIc1pw+2wkxNvRDIxP50rFpfjtBvHNf1vLJqYl8a26rZEpc3lOL5AYbMZ/PW2M3E7U/A+Z5VC0xD2WGtvBk8VlC3q+XrBDGuzbX8DZJX0Pu+1H8PBV62mJVNXwdu/soKbpwomf/r4vkOKGtT/24Zh5BmG8bBhGDsMw9huGMbphmEUGIbxD8Mwdscf87sd/2XDMPYYhrHTMIyLTtzwRURERORk8nSEye2cFpmotKVx4YIy/ucDlUkcWWoqz02n0RfCF7TWArrsx78NQnG2OxGYU0p2GXgHWWl74Zvwi/Os50eHtsIZ1mPnurZICPY8b20nEIvC4Teh8ib49Jsw5QzAhE1/sI6dNIxtAkaBwUb0HwJPm6Y5F1gCbAe+BDxvmuYs4Pn4zxiGMR+4AVgAXAz81DCM8b1Jh4iIiMgY0dYRTgSGvHQXhgGTCzKSPKrU1dlB8mBTOwDO42j5n/KyyyDQCuHAwMfued4KZTYHlC3u+V7+NOux5YD1+Or/wu+vhXvPhG2PWlW4yWdY73UGvHd+C66s3gFwjBgwtBmGkQOcDfwSwDTNkGmarcBVwP3xw+4Hro4/vwp4yDTNoGma+4E9wMqRHbaIiIiIJIMnEE40IMnNcHL/P6/kxpWTkzyq1DUh3jDkYLMfAOdYnj6aVWY9DmZdm7cG5l0Jn1wLmUVHXafYevQ3WAHwzfusYOetgUc/Zb035XTrsSAe2vz1cMrNY2Ij7b4M5k/NdKAB+LVhGBsNw/iFYRiZQKlpmjUA8cfOCacTgcPdzq+KvyYiIiIio5ynI0JOt1b/Z88u7nOTbbEkKm2NVqXNlUpt+kdadjy0DdRBMhoBXx0Uz+m7MubOsbYP8DdaHSL9DXDhXbD6yxAJQO5kyK2wjk3LsdbSOdLgjM+O7PdJIYNpROIAlgGfMU3zDcMwfkh8KmQ/+qr59mr9YhjGJ4FPApSWlrJ27dpBDGV88fl8ui9DpHs2dLpnw6P7NnS6Z0Oj+zU0ul9DN5x71hY02VHTzjkVjnF5v4dzz0JR69fgrYfqAXj3nQ007h6bwS3TV8UKYMsbz9O4ryPx+tH3zRVs4gwzxq4aL9X93M/T7dk0792Cfddb5LhLWH/QxDBnckrmFNoy57O723lTis4l4sjkyIbtWKu4xp7BhLYqoMo0zTfiPz+MFdrqDMMoN02zxjCMcqC+2/GTup1fAVQffVHTNO8D7gNYvny5uXr16uF9gzFs7dq16L4Mje7Z0OmeDY/u29Dpng2N7tfQ6H4N3XDu2Xee2kHU3MtXP7CKaUUp2HL+BBvun7Psl5/BFzWAGGecfurYvXe+BfD2v7JwchGcujrxcq/7VrUBXofZy1cze87qo69i2TGB8hwHdEQhcw6r16yxXj9nA1k2BxN7tPW3rjFrBL9Kqhkw5pumWQscNgxjTvyl84BtwOPAR+KvfQR4LP78ceAGwzDchmFMw7p/b47oqEVERETkpOoIRfn9+oNcuqh87IaOE6Qg00VLu7W/3ZhuRJJRaDUW8dYc+zhvvJ6TXd7/MZnF1vRIb03P4+zOMbkP20AGu0/bZ4AHDMNwAfuAf8YKfH8yDONjwCHg/QCmaW41DONPWMEuAtxummZ0xEcuIiIiIifNwWY/vmCEixeWJXsoo05+hivRPXJMr2mz2az1Zb74mjZ/Y+8mIwCeeKjLmdD/tTKKrD3fvHWQc4xwN04MKrSZprkJWN7HW+f1c/y3gG8Nf1giIiIikkpq2qw27uW56UkeyeiTn9HVqGXMbz6eXQaeI9YUyF+cZ3WHPJq3GmxOK5j1J7MY2qrAjB27IjdOjPE/NSIiIiIyHO8ebiUSjSV+rmntDG1pyRrSqJWf6Uo8d47lShtAwXRr/7XqdwATDrzS9V57M/zhemtz7OwyqzLXn8xCK7BBV1fKcWyw0yNFREREZJzYXeflqp+8yv9eX0k4GmPzkTby0p3YDCjOdid7eKNOQcY4Cm2FM2Hzw1C31fr5yAYoXmg9P/wm7Hrael4xwDbO3atw2ceYRjlOjPE/NSIiIiIyVOv3NwOws87L4+9W8+Cbhzjc0kFxtnvsh44ToGelbYw30SicCZiw+x/Wz0c2dL3nqep6PtA6tczirueqtCm0iYiIiEhPGw5YoW1/g5899T7CUZPX9zZRpvVsw5Ifr7S57DaMsd75sHCG9dgZ0FoP4gx5rOdtVWDYAANyJ/V5ekL3BiYKbZoeKSIiIiI9vX2wBYAt1W2JBiS1ngCVk/KSOKrRqyDTakQy5qtsAAUzup5PORMOvsrMPT+HCtMKbbmT4IofQvHcY1+nM7RlFIJDU3IV2kREREQkoc4ToKqlgyy3g6qWjh7vlakJybB0VtqcY71zJEBaTlfb/4XXQdVblNavgxdqrQCWOwlmrBn4Op1r2tQ5EtD0SBERERHp5p14le2yRV2/LHe2qZ+Qp9A2HAWZXdMjx4XCmdbjhEr4+PMcmXAxNO629l3LnTi4a7izwe7W1Mi4cfInR0REREQGY2edF8OASxZZvyzbbQarZlpVD61pG57ORiTjpolL57q2wplQvpiW/KWACf4GyK0Y3DUMwzp/oGmU44SmR4qIiIhIwoFGPxNy05lfngPAlIIMFkzI4YUd9dqjbZjy0q01bWN+Y+1OlR+ypjem5QLgy5rW9V7OICttAB99yqq2iSptIiIiItJlf1M7U4syKM52k+myM704i9NnFJLtdjC9KDPZwxuVHHYbOWmO8dGIBGDyqXD+1xM/BtJKwG0FuAG7RnaXlgtO/UUBqNImIiIiIt0caPRzxZJyDMPg61cuYFpRJiumFrD5Gxcle2ijWkGma/xU2o5mGFC2CA6+MvjpkdKDQpuIiIiIANDiD9HWEWZqoVVR+8DyIVRF5Ji6b7A9LiVC2xCmR0qCQpuIiIiIALC/yQ/ANE2DHHEfWD6JSDSW7GEkz2m3Qun8xDo3GRqFNhEREREBrKmRAFMV2kbcjSsnJ3sIyZU/1fqfDMs4nVgrIiIiIkc70OjHZsCk/IxkD0VEulFoExEREREADjS1MzE/ffw2zBBJUfonUkREREQAaPQFKclWi3WRVKPQJiIiIiIANPtDFIz3LociKUihTUREREQAaPKHKFRoE0k5Cm0iIiIigmmatKjSJpKSFNpEREREBE9HhEjMVGgTSUEKbSIiIiJCkz8IQGGWQptIqlFoExERERGa/SEACjLdSR6JiBxNoU1EREREaIqHNjUiEUk9Cm0iIiIi0q3SptAmkmoU2kREREREoU0khSm0iYiIiAhNvhCZLjtpTnuyhyIiR1FoExERERGa/UEK1DlSJCUptImIiIgITf6QOkeKpCiFNhERERGh2R9S50iRFKXQJiIiIiI0+0NqQiKSohTaRERERMY50zRpUqVNJGUptImIiIiMc/5QlFAkpkqbSIpSaBMREREZ55p92qNNJJUptImIiIiMc03+IACFavkvkpIU2kRERETGuWZ/Z6VNLf9FUpFCm4iIiMg41xQPbWpEIpKaFNpERERExrmuSptCm0gqUmgTERERGeea/SHcDhsZLnuyhyIifVBoExERERnnmnzWHm2GYSR7KCLSB4U2ERERkXGu2R+kQJ0jRVKWQpuIiIiMWZFojN+9foBwNJbsoaSkF3bUcfH/rqOqpUOdI0VSmEKbiIiIjFlvHmjmPx7byiu7G5M9lJT09JZadtR62V3vU+dIkRSm0CYiIiJjljcQAeBIa0eSR5KaNh1uTTxX50iR1OVI9gBERERETpT2kBXaqhXaeumImOyu9yd+VmgTSV2qtImIiMiY5QtGAYW2vhxoi2GasHpOMaCNtUVSmUKbiIiIjFntwXilrS2Q5JGknr1tVqD9/IVzKMpysWBCbpJHJCL90fRIERERGbP8QU2P7M9BT4wphRksnJjL21+7INnDEZFjUKVNRERExix/yKom1bYFiMbMJI8mtXiCJqU5ackehogMgkKbiIiIjFmdjUgiMZMGbzDJo0kt7RHISXMmexgiMggKbSIiIjJmdTYiAahu0xTJ7joiJjlpWikjMhootImIiMiY1R6M4HJYv+5oXVtPHRGTbIU2kVFBoU1ERETGLF8wwvSiTACOtCi0dTJNk/Yw5KRreqTIaKDQJiIiImNWeyhKeW4amS47tR61/e/kD0UxQZU2kVFCoU1ERETGLH8oQobbQUlOGvVqRJLg6QgDakQiMlootImIiMiY5Q9GyHI5KM520+BRaOvkDVhdNbMV2kRGBYU2ERERGbPag1Ey3HYrtPkU2jp5A1alTdMjRUYHhTYREREZk0zTxB+KkOV2UJLtpl5r2hI88dCmRiQio4NCm4iIiIxJgXCMmAkZLgcl2Wn4Q1H8wUiyh9XDvgYfpmme9M/tmh6pSpvIaKDQJiIiImOSP2QFkyy3nZJsN0BKNSPZXuPh3O+/xAs76k/6Z3sU2kRGFYU2ERERGZM6q2oZLgclOfHQlkJTJDcdbgXgjf3NJ/2z1T1SZHRRaBMREZExyR+MApDptqZHQupV2gA2HWo96Z/tDURwGJDmtJ/0zxaRoVNoExERkTGpc3pkZgpPjwR470gr4WjspH62JxBGPUhERg+FNhERERmTuk+PzMtw4rLbqPemxvTIWMxke42Xoiw3gXCMnbXek/r53kCEDIdxUj9TRIZPoU1ERETGpPaQNT0yy+3AMIxBb7D9hzcO8bVHN/d47aVdDfzi5X0DnhuLmYQiA1fNqlo68AUjXL+iAoCN8fVtJ4s3EFZoExlFFNpERERkzLnriW3825/eBSDDZa3bKs52D2p65LPbavn9+kPUtHUA1n5v33xiGz98bveA5/7q1f2s+d7aAdv4b4tPjbxgfhmlOW7W720a8NojydMRJkPTI0VGDYU2ERERGXOe3FxDR7ir0gZQlOWiyR8a8NxGnxXs/v5eDQCbj7Sxu96HNxjBN8A+bxsPtXKktSNR5euLaZo8vOEwGS47c0qzWTOnhJd2NQyqQjdSvIEI6aq0iYwaCm0iIiIypviCEWrautauZbitSltOujPR6v5YGr1WsPv7Ziu0/WVDVeK92rZjr4k70OQHoPkY4fCZrXU8t72eO86fTbrLznnzSvEFI7x5Elv/ewJhhTaRUUShTURERMaUfQ0+AM6eXczp0wtxO6zQlpvupG2A0GaaJk3+IBkuOxsPtVLvDfCPbXWJ7pPHCm2maXKwqR04dmj78Qu7mVWSxc1nTgVg1cwi3A4bz22vG/R3PF5WI5KT9nEicpwU2kRERGRM2RsPbf95+Twe/ORpiddz0534ghEix2iv39YRJhw1OXVaAQBbj3iobgtw9uxiAGqPsTl3kz+UmD7ZX2g72ORna7WHDyyfhNNu/RqW7rJz5swint9RN+BauIGs29UwYDANhKO0h6JkuVRpExktFNpERERkTNlb78duM5hckNnj9dz4xmSeQP/r0jrXsy2faoW2tTvrAThteiEAtfHmJH05GJ8aCfS7du6pLbUAXLywrMfr580r4XBzB7vrff1efyAt/hAf+fWbAzZMaWm3xpat0CYyaii0iYiIyJiyp97HlMIMXI6ev+Z0hrZjVaIa4uvZFlfk4nLYeCEe2uaWZZOX4TxmpW1/Y3viebO/7y6VT26uYUlFLpMKMnq8ft7cUoDjmiK5p8GHacIzW2uPWbFr8im0iYw2Cm0iIiIypuxt8DGjOKvX64MJbU3xsFWSncbUwgwON1uVtalFmZTlpB1zTdvBJj82A5x2o89K2+Hmdt6rauOSReW93ivLTWPhxBye315/7C93DHviVbojrR1sOeLp9zhV2kRGH4U2ERERGTOiMZMDTX6mF2f2em8woa0xvo9bUZaLaUWZ8edustwOynLTjllpO9DUTkV+BgWZLpp9vUPb0/GpkZcu7B3awKq2vXOohSbfwHvJ9WVvvQ+X3YbdZvD01pp+j+tcb5flVGgTGS0U2kRERGTMaPAGCUdNJuVn9HovL76bdGt7/50dG30h7DaD/AwX04qsat20Iuta5bnHrrTtrvMytSiTgkx3n41I/r65hoUTc5hc2HtsAOfPK8U04cWdDf1/wWPY2+BjRkkWp00v4Kkt/U+R7JwemaNKm8ioodAmIiIiY0ZnJawsJ63XezmdjUiOVWnzBSnIdGGzGUyPV9qmFlqPpTlpNPpCfW6C3egLsqPWy8qp+RRmWpt43/vSXnbXealtC/DtJ7ez6XArl/RTZQNYODGH0hw3L+yw1rU9s7WWX72yP/G+NxA+5gbcexp8zCjO5OIFZexr8CemSx6tpT2EzYB4hhWRUUA7dIiIiMiYURcPbaV9hLZBTY/0BSnKsvZkm9oZ2uKPnUGwzhPo1Ujk1T2NAKyaVcyuOh8bDraw6XArB5vaSXPauP+1A5w+vZAPLJ/U72cbhsG5c0v527vVPL2lltv/8A7RmMmKqQVMK87k4v99mdVzivnWNYt6nPfctjoef7eaqpYOrl1awYULyviPx7by9JZaZpVm9/qcJn+I/AwXNkOVNpHRQpU2ERERGTMSoS3X3es9t8NOmtN27O6RvhBFWS4A5k/IYdnkPM6J79FWkGm93v38jlCUX7+6n6c215Kb7mTRxFwKMl10hKMAbDjYzJv7mzl1WiEPfvI0irN7j6u78+eV4AtGuPX3G5hVkkVehpPvPL2dbz+53WowUt27wciTW2p4/N1qTBNmlmRRmpPGssl5ie0FjtbiDyW+i4iMDqq0iYiIyJhR2xbAbjMoyuw7HOWmO2nrCFPvDVCS3bsa1+gNMiNeWctyO/jrp85MvJed1nt65d/ereYbf9sGwKWLyrDbjB6BaFedD5sBnz531qDGf/bsYj577kzK89K5dGE5f95wmG/+fTuv7mnCaTc40OjvdU5Na4DsNAexmEnlpDwALphfxnef3kGDN9grKDb5Q+RnuoDhNTwRkZNPoU1ERETGjDpPkJJsNzZb31P/ctOdPLm5lj+9XcWvbl7OufH90Tq1dYTJy+i7CpWTbv3a1H1z7i3VbaQ77UwpzOD98amPnaFtYl46R1o7iJmwMr5Z90Ccdhufu3BO4uePrZrGsin5HGnp4ECjn+//YxctidBlqW7r4JzZxfzwhqXY49/7tOnW572xv4n9DX7WzC1h4cRcwKq0zSzJQqFNZPTQ9EgREREZM+o8gT7Xs3XKTXfiC1qh60hrz06Q0ZiJLxghK63vv9PO6ay0BboqbVuOtLGoIpen//Vs1swpAaAwHqg+tmoaDpuB3WawdHLesL6PYRgsm5zPFUsmMK88B4D9TV3VtljMpKYtwMS89ERgA1g4MZdMl52fv7yf7/9jF195ZHOim2TzUaFPRFKfQpuIiIiMGXWeQJ+dIzt1NiMByDkqnPlDkT5fTxx/VPfJaMxkW42HBRNyehx3ytR8LphfylWVE1gyKY8lFblkuo9/ctO0+N5z+xu6QluT3+pmOSEvvcexTruNU6YW8O7hVgDeq2rjxZ31xGImLe2hRLAUkdFBoU1ERERGtcPN7bQGrFb4tZ4ApTn9N/vI6RbaguGe7fO98WmP2f2Etmy3A8Pomh65r8FHIBxj4YTcHseVZKfx839aTmGWm5/ctIyffvCUoX+pPkzKz8BmwMu7G/jyXzcTCEepaesArD3kjtY5RfJ9p1QwuSCDbz6xnaoWa7qmGpGIjC4KbSIiIjKqfeTXb/KTTUHaQxG8gQilfQSYTt0rbcFItMd73vi0xyx33xuY2WwGWS5H4rit8U6OnWvF+lKWm0bZMcYzFC6HjYr8DB7dVM2Dbx5ia7WH6lYrtB1daQO4YF4pRVluPrZqGne/bzGHmtv56P1vAQptIqPNoEObYRh2wzA2GobxRPznAsMw/mEYxu74Y363Y79sGMYewzB2GoZx0YkYuIiIiMiBRj/7Gvzsbo3x+t4mAEr76ArZKb9bk5FgZGiVNrAqdZ4O67it1W24HTZmxKctngyde8YBVLd2UB1fl9dXaJtVms3bXzufeeU5nDa9kLvft5hmfwi7zYg3IhGR0WIolbZ/AbZ3+/lLwPOmac4Cno//jGEY84EbgAXAxcBPDcOwj8xwRURERLqs290AgAH89zM7AY5Z2bp22US+fsV8oHdo8w0itGWnORKNSPY3+plamInDfvImLp02vSARuI60dlDd2kGa00Z+Rt/Vwe6uXVbBO/9xAdv/62IWTOi/OigiqWdQ/5YxDKMCuAz4RbeXrwLujz+/H7i62+sPmaYZNE1zP7AHWDkioxURERHpZt2uBiYXZLC42M6OWi9LJ+dxypT8fo+vyM/g5jOmAr1DW2cY69yPrS9Wpc067mBTO1MKM47zGwzNp1bP5B93nE1OmoPq1g5q2gJMyEvHMPre4qAvLodWx4iMNoNtZfS/wL8D2d1eKzVNswbANM0awzBK4q9PBNZ3O64q/pqIiIjIiAlHY7y2t4nrllWwyFXPh1fP5urKCQNWvgzDwO2w9bGmbRDTI9OcVLd2EIuZHGpuZ/Wc4uP/IkNkGAYT8tKpbu2gwRdiQm7vqZEiMrYMGNoMw7gcqDdNc4NhGKsHcc2+/qrH7OO6nwQ+CVBaWsratWsHcenxxefz6b4Mke7Z0OmeDY/u29Dpng2N7tfA6ttjtIeiuHw1ZOQFyfLu4ZWX9wzqXDsx9h04xNq1dYnX3tsXAmDTm6/jdvRduWpvC1LfGuXRZ18kGIkRbDrC2rX1x/9lhigtGmDbYR+N7SbnTHIM68+K/owNne7Z8Oi+Hb/BVNrOBK40DONSIA3IMQzj90CdYRjl8SpbOdD5b6wqYFK38yuA6qMvaprmfcB9AMuXLzdXr149/G8xRq1duxbdl6HRPRs63bPh0X0bOt2zodH9Gtgb+5pg3XrWnLqUyJEtQ7pfGa88R1FpKatXL0q89lZwB/Y9+7jwvNX9Tjdc69nKluYqJsxeAmvXc8HplZw16+RX215o28JvXz8IwBVnLGL1kglDvob+jA2d7tnw6L4dvwEnNZum+WXTNCtM05yK1WDkBdM0PwQ8DnwkfthHgMfizx8HbjAMw20YxjRgFvDmiI9cRERExrVaj9U5cTgt9fubHpmd5jjm+rCcNAfeYIQDjdYG11MKTl7nyO66d4tcOikvKWMQkZNnsGva+vId4E+GYXwMOAS8H8A0za2GYfwJ2AZEgNtN04z2fxkRERGRoatp6wptVUM81+209dnyP8t97F+NctKdmCZsq/HgsBlMyBuZPdiGamI8tBVluajI15o2kbFuSKHNNM21wNr48ybgvH6O+xbwreMcm4iIiEi/atsCZKc5BgxafXE77IT6CG3H6hwJViMSgM1H2qjITz+p7f6766y0VU7KG1LnSBEZndTzVUREREalmrYOyocxNRI6p0ceHdrCx+wcCV2dJbdVe5hcmJypkQCTCqzQtnRy/9sbiMjYcTzTI0VERESSprYtQNkw2927HTaC4d5r2gYKgTnpVqUtGIlxxozCYX32SCjJTuP+j65k+TH2pBORsUOVNhERERmVatoClOcMs9LmtPeqtPmCkQErbZ3TIw0DrqocesfGkXTO7GIyhzE1VERGH4U2ERERGXXC0RgNvuCwOkcCuOz9TY8cYE1buhWSzphRSLk2tRaRk0ShTUREREaVaMykurUD02T4a9qcPVv+m6aZaPl/LCXZaUwuyODmM6YN63NFRIZDNXUREREZVX7wj13c+9JeYHh7tIG1pq1798hAOEYkZpI1QGhLd9lZ9+9rhvWZIiLDpdAmIiIio8pTW2qw2QwcwIzirGFdw+3ouabNGwwDDDg9UkQkGRTaREREZNSobQuwt8HPVy6dy/XLJ5ObMbyQ1b17ZDAS5fevHwQgZ4BKm4hIMujfTCIiIjJqvLqnEYAzZxYNO7BB55o2q9L2vWd28vOX97NmTjHnzysdkXGKiIwkhTYREREZNV7d20hBpot5ZTnHdZ3O6ZFHWju4//WDXLesgu9/YMkIjVJEZGSpe6SIiIiMGpsOt7J8Sj42m3Fc13E7rF+B7nlhN5hwxwWzRmJ4IiInhEKbiIiIjBrN/tCwO0Z21xnathzxsKgil4r8jOO+pojIiaLQJiIiIqNCNGbS1hEmL/34Ozx2hrZaT4D8DNdxX09E5ERSaBMREZFRwRsIY5qQNwIhy+2wA9DoC5J/HA1NREROBoU2ERERGRVa2q291PIzR6DS5rR+BTJNyM9UpU1EUptCm4iIiIwKLe0hYKQqbV2/Aml6pIikOoU2ERERGRXa4pW2kVnTZk881/RIEUl1Cm0iIiIyKnRW2kaiMubqVmkbicqdiMiJpNAmIiIio0JiTduIT49UpU1EUptCm4iIiIwKbe0hbAZkpzmO+1o9pkeqEYmIpDiFNhERERkVWtrD5KY7sdmM475WZ/dIUCMSEUl9Cm0iIiIyKrS0h0YsYLl7rGnT9EgRSW0KbSIiIjIqtHWEyR2hgNU5PTLb7cBp169DIpLa9G8pERERGRVORKUtbwQ26hYROdEU2kRERGRUaPGHR2wqY2fLf61nE5HRQKFNRERERoW2jjB56SNbaVNoE5HRQKFNREREUl4oEsMXjIzYnmoOuw27zdAebSIyKii0iYiIyIjYcqSNjlD0hFy7urUDGNk91bLTHJTkpI3Y9UREThSFNhERETlu/mCEa376Kr9+bf8Juf4DbxzEbjNYPad4xK55/z+v5Jazp4/Y9UREThRHsgcgIiIio1+dJ0A4arLlSNuIX9sTCPPgm4e5fHE5FfkZI3bdJZPyRuxaIiInkiptIiIictwavEEAttd4R/zaT2+uxReM8NEzp434tUVERgOFNhERETluDT4rtB1o8tMeiozotTdVtZKd5mDRxNwRva6IyGih0CYiIiLHrd5jhTbThMc2VfPrV0dubdvmqjYWV+Risxkjdk0RkdFEoU1ERESOW2elDeCrj2zmG3/bhicQPu7rBiNRdtR6WDQx77ivJSIyWim0iYiIyHFr8AYpy0kj02UnZlqv7a7zHfd1d9Z6CUdNFldoaqSIjF8KbSIiInLc6r1BSnPcrJhWwNyybAB21x1/U5J3q6xulFrPJiLjmUKbiIiIDEo4GsMf7GoyUt3aQbM/BFiVtuJsNz//p+U89ukzSXfa2TkCoW1btYe8DCcV+enHfS0RkdFKoU1EREQG5Qf/2MWlP3qZWMwkGIly3f+9xr/9aRPQGdrScNptuB12ZpdmJaZHPvFeNY9tOjKsz+ycdmkYakIiIuOXNtcWERGRQdld7+NgUzvbajxsq/FQ0xagyReirT1Mk9+qtHWaXZrN2l0NANy3bh/N/hBXVU4c8me2tIcoyHSN2HcQERmNVGkTERGRQWmMd4h8cUc9963bR06ag1A0xmPvHsE06RXaGrxBWvwhqlsDVLV0JM4fipb2EPkZCm0iMr4ptImIiMigdIaue17cw556H3ddvZCcNAcPvnkYgOKsrtA2qzQLgG01nsR5mw61DvkzW9vD5GU4j3PkIiKjm0KbiIiIDEqjN4TdZhCMxLhoQSlXLpnA6jklbK/xAFCWm5Y4dnqRFdre2N+ceO2vG6u4/Mcv89TmmkF9Xixm0qpKm4iI1rSJiIjIwPzBCB3hKDesmERre5hvXrMQwzD44iVzqZyUR066k8Xd2vJPyEvDYTNYv7cJAMOAJzfXAnDbA+/wk5uWsXBiDj98fjdVLR0UZbnYcsTDpIJ0Hvj4aQB4AxFiJqq0ici4p0qbiIiIDKhziuMpU/K598OnUBSfCjkxL52PrprG+06pwGbr6vDosNuYVJDBpsOtAKyYUgDA1y6bx6SCdP76ThW/e/0gj2+qJhYz2VbtweWw8eqeJrZVW5W7lnZrOwFV2kRkvFOlTURERAbUGdqKujUbGciUwgz2N/oB+NyFs3lhRz3/fOY0dtf5eHprLTVtAZZPzeehT54OQGt7iJXfep4/bzjM1ycs6Aptmaq0icj4pkqbiIiIDKjBawWo7s1GBjK1MBOAbLeD06YX8pVL52G3GayYVkBbR5htNR5Om16YOD4vw8UF80t5dOMRQpFYIrTlqdImIuOcQpuIiIgMKFFpG0Jom1KYAfRsUAKwcmpB4nn30Abw/uUVtLSHeX57HS3+MAAFCm0iMs4ptImIiMiAOkNbYdbgA1Rnpe3o0DapIJ3SHDcuh43KSXk93jtrVjFlOWn8eUOV1rSJiMRpTZuIiIgMqNEXJC/DidM++L/v7ay0lR8V2gzD4LplFTT6gqQ57T3es9sMrl02kXtf2ktRlgubAdlp+nVFRMY3/VtQREREBtToDQ1paiRARX4G2W4HM4qzer337xfP7fe8951SwU/X7uWxTdXkZbh6dKUUERmPFNpERERkQI2+IEVDmBoJ4HLYePZzZ1OQObTzphdnMbs0i111Pibmq3OkiIjWtImIiEifdtR6uPPxrXgCYQ42t1OSnTbwSUcpz03H7bAPfOBRzp9XCmg9m4gIqNImIiIiffAGwtzyuw0cbGpn46EWGrxBrlwy4aR9/vnzS/np2r3kZ6jSJiKiSpuIiIj0cs8Lezjc3M6SilzerWpjblk2584tOWmfX1mRx8S8dCYVZJy0zxQRSVWqtImIiEgvu+t9zCvP4Z6blnHNT1/jjgtmn9SGIDabwd8+s4p059CnVoqIjDUKbSIiItJLky9IYZabSQUZvP2185MyhqE2MBERGas0PVJERER6afKHKFRoEhFJCQptIiIi0kuzP6RKl4hIilBoExERkR4C4SjtoahCm4hIilBoExERkR6a/CGAIW+mLSIiJ4ZCm4iIiPTQ5AsCUJDpTvJIREQEFNpERETkKJ2VNk2PFBFJDQptIiIi0kOzzwpt6h4pIpIaFNpERESkh+Z4pa1Qa9pERFKCQpuIiIj00OgP4rLbyHI7kj0UERFBoU1ERESO0uyz9mgzDCPZQxERERTaRERE5CjaWFtEJLUotImIiEgPTf6Q1rOJiKQQhTYRERHpodkfUudIEZEUotAmIiIiCZFojJq2Dsrz0pM9FBERiVNoExERkYTDLR2EoybTizKTPRQREYlTaBMREZGEfQ0+AKYXZyV5JCIi0kmhTURERBL2N/oBmFGsSpuISKpQaBMREZGEvQ1+8jOc5GWoEYmISKpQaBMREZGEfQ0+TY0UEUkxCm0iIiKSsK/RryYkIiIpRqFNREREAPAGwjR4g6q0iYikGIU2ERERAeDtgy0AzC3PTvJIRESkO4U2ERERAeCJd2vITnNwxozCZA9FRES6UWgTERERgpEoz26r5cL5Zbgd9mQPR0REulFoExEREV7Z3Yg3EOHyJeXJHoqIiBxFoU1ERER480AzTruhqZEiIilIoU1ERETYVu1hdmm2pkaKiKQghTYREZFxzjRNtlZ7WDAhJ9lDERGRPii0iYiIjHO1ngDN/hALJuQmeygiItKHAUObYRiTDMN40TCM7YZhbDUM41/irxcYhvEPwzB2xx/zu53zZcMw9hiGsdMwjItO5BcQEREZz0zT5HvP7GRPvW/Y19h6xAOgSpuISIoaTKUtAvybaZrzgNOA2w3DmA98CXjeNM1ZwPPxn4m/dwOwALgY+KlhGJogLyIicgIcbu7gnhf38NvXDwz7GlurPRgGzCtXaBMRSUUDhjbTNGtM03wn/twLbAcmAlcB98cPux+4Ov78KuAh0zSDpmnuB/YAK0d43CIiIgLsafAC8Oqexj7fX7uzns/9aRO/fGU/zf4QL+yo460DzT2Oea+qlWlFmWS6HSd8vCIiMnSGaZqDP9gwpgLrgIXAIdM087q912KaZr5hGPcA603T/H389V8CT5mm+fBR1/ok8EmA0tLSUx566KHj/Cpjj8/nIysrK9nDGFV0z4ZO92x4dN+GTvdsaAZ7v57aH+aPO0MA/GB1Otkug9erIywtcfBGbYTfbwvhtkMgCjYDYibkug1+sDodm2EQiZl8+vl2Tp/g4CML3Cf6a50w+vM1dLpnQ6d7Njy6b4OzZs2aDaZpLu/rvUH/lZphGFnAX4B/NU3TYxhGv4f28VqvZGia5n3AfQDLly83V69ePdihjBtr165F92VodM+GTvdseHTfhk73bGgGe7+ebHwXm1FFzASjdA4+4JfPbiLdGaUjHGXNnGLuuWkZR1o7+MMbh/AEwvz1nSNUpU3je8/s5J/PnEYgupvrz1nC6oVlJ/x7nSj68zV0umdDp3s2PLpvx29Qoc0wDCdWYHvANM2/xl+uMwyj3DTNGsMwyoH6+OtVwKRup1cA1SM1YBEREemyp97H8ikF7Kzz8sqeRpx2G1luB8um5HPWzCI+tmoaNpvB7NJs7rxyAb5ghCfeq+HOx7cSM+GHz+/GZsDp2lRbRCRlDaZ7pAH8Ethumub/dHvrceAj8ecfAR7r9voNhmG4DcOYBswC3hy5IYuIiAhYnSP31PuYVZrF6jnF/GNbHa/tbWTltAJ++9GVfOLs6dhsPSfAZLkdrJ5dTMyE5VOsxs+Vk/LITXcm4yuIiMggDKbSdibwYWCzYRib4q99BfgO8CfDMD4GHALeD2Ca5lbDMP4EbMPqPHm7aZrRkR64iIjIePbOoRae2lyDJxBhZkkWU4syeWxTNW0dYW5cOfmY535s1TRshsH3P7CE2x54h8sXlZ+kUYuIyHAMGNpM03yFvtepAZzXzznfAr51HOMSERGRY/jZS3t5ZmsdALNKsjltegFFWS4afSFWTis45rmnTi/k1OnWdMjfflQNnkVEUt1g9mkTERGRFPPu4TYqJ+Vx8xlTWT41H4fdxnXLKsjPcLJwQm6yhyciIiNIoU1ERIal2R/iu0/vwBsIJ3so406dJ0CtJ8AVSyZw55ULSHPaAfj8RXN47nPn4HLoP+8iImOJ/q0uIiLD8vimI/zf2r18+a+b8QUjxGKD3/dTjs+7h1sBqJzUs6LmtNsozBq9e62JiEjfFNpERGRY3j7YgmHAE+/VsPDrz/D5h99N9pDGpDsf38ovXt7X47V3q1qx2wwWaBqkiMi4MOjNtUVEZGz7f09tZ8WUAs6fXzqo4zccbOHSReWcNr2QP711mNf3Np3gEY4/gXCU368/SCRmEorGmFWSzXlzS9h4qJW5ZdmJaZEiIjK2KbSJiAiRaIxfvryfek9wUKHtSGsHNW0BVkzJ58OnTSEYjvLNv2+nwRukOFvT80bKliNtRGImBZku7n56JwBXV07gtb1NfObcmUkenYiInCwKbSIiQp03SCRm0ugLDur4tw80A7B8qtVaftFEa5reliNtrJlbcmIGOQ5tiq9de+Izq/AGInz/2Z08uqmaSQXpfGq1QpuIyHih0CYiIhxubgeg0Rca1PHvHGwhw2Vnblk2AAsm5mIYsFmhbURtOtzKxLx0JuSlA3D3+xZj+8tmPn7WNNJdmhopIjJeKLSJiAhVLR0Ag6+0HWxh6eQ8HHarn1WW28H0okzeq2o7YWMcjzYdbqVyUl7i57wMF/d++JTkDUhERJJC3SNFRISqFqvS1uwPDdi63xeMsL3GwylTCnq8vmhiLu9VtWKaav0/FOFojP2N/l6vNwdiVLV0sGSSOkSKiIx3qrSJiEii0haNmbR2hCnIdPV77KZDrcRMWD4lv8frZ80q5tFN1azf18zpMwpP6HhT3cZDLfiDUVbNKjrmcW0dYT7527d5Y38zt5w9nVml2VS1tDOzJIvnDkawGXDh/LKTNGoREUlVCm0iIpKotIE1RbK/0GaaJm8fbMYwYOnkvB7vXba4nG/8bSsPvnloXIe2WMzkUw+8Q01bgJVTC1g+NZ9PrZlJltv6T+4b+5r4/MPvcsrkfN7Y30yjL8i5c0v42bqee7E5DLhkYTlTizKT8TVERCSFKLSJiAiHmzsoz02jpi1AozfI7NLsXsc0eINc8IOXaA9GmVOaTXaas8f7aU471y6r4A9vHKLRF6Qoa+y0/jdNk9+8doDLF09gV52XN/Y3c8f5szAMo9exGw61UNMW4JKFZexv9HPvS3vZ3+gnFImxp8FHky9EltvBM1vrmFWaxT03LeOUKfnsrPXidtgoyXHzLw9t4rltddxyzvQkfFsREUk1Cm0iIuNcJBqj1hPgwvml1LTV0ujvu4PkY5uO0Noe5owZhVyxZEKfx3zotCn8fv1Bvv3kdv7nA5UncNQn1846L9/42za213h4c38zB5rayc9w8s9nTutxXCxm8rd3q0lz2vje+5eQ6XZwzwu7+d6zu3DYDE6fUUheupN7P3wKpdlp2GxdoW9OWVdQ/ukHl/GXp9eyuCLvZH1FERFJYQptIiLjXE1bgGjMpHJSHk9tqaXR29VBMhYz2dfowzAM/vrOERZX5PKHT5zW77VmlmRx6zkzuOfFPbzvlArOmHHsNV2jxbZqDwB/ersKgIl56fy/J3dw6aJySnPSrPfeOsx/Pr6FUCTGJQvLyYxPh7xt9UxCkRinTi/kzJmDux9Ou42yTPUKExERi/6LICIyztW0BQCYW56Dw2Yk2v6HozHed+9rnP8/6zjv+y+xrcbDdcsqBrzep8+didth44Xt9Sd03CfT9hoPTruBYUBJtpsfXF9JKBrj3cOtBMJRfvbSXr701/dYOCGXq5dO5LbVMxLn2m0Gn7twzqADm4iIyNFUaRMRGedqPVZoK89NoyDTRVN8g+3frz/IO4da+cJFcwhGYqzf28RVlX1Pi+wuzWlnckEGh5rbBzw2lexr8NHaEWbZ5Pxe722v8TK3LIcrlpQzKT+DeeXWVMbd9T4efPMQL+5s4Ny5JfzkpmXa9FpEREacQpuIyDhXHw9tpTlpFGW5WburnrPufoE6T5BVM4v41OoZVsONCwZ/zSmFXaEtEo3x8IYqntsW5FvvvMTc8hx+fOPSE/FVhsU0Tf7vpb384B+7CEdNLl9czg9vWIq923qzHbUezp1bwifP7qqgTchNY3NVG6/saeTmM6Zy55ULkjF8EREZBzQ9UkRknKttC5DutJOT5qAo202dJ0huupNLF5bxzasX9tkhcSCT4pU20zR5bW8TX/rrZtYdiWAY8Ld3q3l5d8MJ+CbD84uX93P30zu5YH4pN58xlSfeq2FrdRt76r00+YLUewM0+kLMK8/pcd7M0mxe2FFPOGpq6qOIiJxQqrSJiIxztZ4ApTluDMPgrJlF2A2456ZliUYawzGlIIP2UJRGX4htNVYTj/85J4MLzl3Fud97ie88tYMzZhT1qGYlwyu7G/nWk9u5bFE5P75xKXXeAL957QBv7Gvmp2v3MLs0m1vPsaprc8t6hrbZJVms22WFz2VH7VknIiIyklRpExEZ5+o8gUQHxE+cPZ1f//PK4wpsAFMKrQ2hDzX72VnrpTw3jSyXgdth54uXzGVrtYffvX7geId+XDyBMP/+8LtML87k+x9Ygs1mUJ6bzsS8dH7z2gFa2sO8sb+ZL/31PYqyXCyuyO1xfudedtOKMikcQ3vSiYhI6lFoExEZ5+o8Qcpy00b0mpMKMgA41NzO9hpPjz3Irlhczjmzi7n7mZ3UxjtXJsMf3jhEdVuA779/CWnOruYhy6bkc6S1A6fdoDTHmi76nWsX9wqys0qzAFiqKpuIiJxgCm0iIuOYaZrx6ZEjHdrSMQzYW+9nb4Ovx9RCwzD40iVzaQ9FeXVP44h+7lC8tb+ZGcWZLD2qW2TnVMfTphfyk5uW8Z1rF3H+/NJe588py2ZCbhoXzi87GcMVEZFxTGvaRETGsdb2MKFIbMRDm9thpzwnjRd3Wo065pVnQ2tt4v0ZxVnYDDjY5B/Rzx2sWMxkw6EWLuwjjK2YWgDAeXNLWD61gOXxn4+W4XLw2pfPO6HjFBERAVXaRETGtTqvNT2xbIRDG8CCiblsrbaakHSfHgngctiYmJ/Ogabk7OW2r9FHa3uY5VN6B7KFE3O5/6MruenUKUkYmYiISG8KbSIi41jnmrLSnJFvpHH3dYs5c2Yhxdluphdl9Xp/amFm0iptGw62AHDK1N4baQOcM7sYl0P/iRQRkdSg6ZEiIuPYliNtACM+PRIgP9PF7z92KsFIrM8ANKUwg8c3VeMNhIlETfIzXSM+hv68faCF/Awn04syT9pnioiIDJf+GlFEZJy6b91evvfsLpZPyWdCXvoJ+QzDMHp0ZuxuamEmnkCEj/zqTc66+0We21Z3QsbQl63VHhZV5A1r43AREZGTTaFNRGSceuitw6ycWsAfPnFaUja5nhrfy+2dQ61EYya3PbCBFn/ohH9uJBpjT4OPuUetsxMREUlVCm0iIuNQsz/EvgY/q+cmb+3W1KKMxPOvXjaPcNRMNC45kQ42txOKxBKbY4uIiKQ6hTYRkXFo4yGrEceyyX034jgZKvIzMAxYMimPSxeVA7C95sSHtl21XgBV2kREZNRQIxIRkXHonUMt2G0GSyrykjaGNKedT62ewanTCinIdFGa4z4poW1HrRebATNLene0FBERSUUKbSIi49CGgy0smJBDuqvvJiEnyxcumpt4Pq88h20no9JW52VqYWa/DVJERERSjaZHioiMYXvqvTy68QgHGrv2Q3tldyMbDrawYmrvjaWTaW5ZDnsbfIQisRP6OTvrvFrPJiIio4oqbSIiY9RTm2u47YF3ALDbDD6wfBJzy7L5zlM7mFGcxafXzEzyCHuaV55NOGqyp97H/Ak5J+QzNh5qYV+Dn+uXTzoh1xcRETkRVGkTERmD/MEI3/jbNuaX5/DEZ1bx4dOm8PCGw3z98a0srsjldx879aRuZj0YlZPyAPiff+wiHB35alsgHOWbf99OcbabD502ZcSvLyIicqKo0iYiMsqFIjE8gTBFWe7Ea796ZT+1ngA/+eAyFk7MZeHEXD62ahoHm9o5Y0YhtiTsyzaQKYWZ/NdVC/jPx7Zyzwt7uOOC2cc8/lBTOy6HjbLctAGv/eTmGv71j5sIRWJ897pFZLr1nz8RERk9VGkTERnF2trDvP9nr3PO3S9ysMlatxaLmfxpw2FWzSzilCldLf0nFWSwalZRSga2Tv90+lTml+fwXlXrMY+LRGO8/2evccEPXmLtzvpjHhuNmXznqR1MK8zktx9dyQc0NVJEREYZhTYRkVHssw9tZHu1B8MwuOOPm2j0BXn7YAuHmzu4dtnEZA9vWKYUZnCouf2Yx7ywo546T5A0p52P3f82j79bjWmafP7P7/Lfz+zocexTW2o41NzOHRfM4uzZxRhG6oZWERGRvmh+iIjIKNXiD/Hy7gY+tXoms8uy+eyDGzn128+Tl+4kw2XnogVlyR7isEwuyOD5HfXEYma/VcGH3jpMaY6bZ//1HD7xu7f5l4c28tLOBv7yThVZbgefPW8WbofV0v9Xr+xnWlEmF8wfnfdDRERElTYRkVFq3e4GYiacN6+EK5dM4LnPnc1t58xgZkkWt6+ZOWrXbU0qyCAUiVHnDfT5fos/xNqd9bzvlApyM5zc/88rWTWziL+8U0VOmgNfMMJre5sA2Nvg451Drdy4chL2FJ4WKiIiciyj87/oIiLj1L0v7eWZrbV8//1LWLuzgYJMF4sr8gCYWZLN5y+ak9wBjoDJBRmA1WikPDe91/tbqtuImXDGjCIA0l12fv5Py7lv3T4umF/K+/7vNZ7dWsuaOSX89Z0qbAZcXTk6p4qKiIiAKm0iIqPG/kY/3392JxsPtXLZj17hyc01rJ5dPOYqSInQ1s+6tq3VHgAWdNvLLc1p57PnzWJeeQ5r5pbwzNY6/MEIj7xzhHNmF1OSM3CHSRERkVSl0CYiMgrEYiZff3wrboedRz51Blcvncicsmw+sGLsdUKckJeOzYDD/YS2bdUeJualk5fR9z5zN66cTLM/xEd+9SbVbQE+fLr2ZBMRkdFN0yNFREaBn63bx7pdDfzXVQtYOjmfpZPzBz5plHI5bJTnph+j0tbG/G5VtqOdMaOQFVPzeetAC6dMyWfNnJITNVQREZGTQpU2EZEU1h6K8PXHtnD3Mzu4bHE5Hz5tfFSNJhdkcKCpd2hrD0XY1+jvMTXyaIZh8G8XzsHtsPHlS+aqxb+IiIx6Cm0iIimg2R/inUMt1LZ1dUxs8Ye46edv8Nv1B/nI6VP53vuWjJsAsmJqPpsOt/KHNw71eP3VPU2YJiyYkHvM80+bXsjWb1zE8qkFJ3KYIiIiJ4WmR4qIJNH2Gg+/emU/j71bTSgSw24z+MU/LWdOWTb/9Ks3OdTczs8+dAoXjtI914brM+fN4r0jbXzt0c2cN6+Eu57Yxit7GmltD1OU5WL5lIGnhzrs+ntJEREZGxTaRESS5KE3D/HlRzaT5rDzgeUVrJlTwv/8Yxe3/n4DhgFOm43ffXQlp04vTPZQTzqn3cZnz5vF2p0NrN/XxFNballSkcsF88u46dTJ5KY7kz1EERGRk0ahTUTkJIrFTO5dt5dnttbx7uFWVs8p5ofXLyU3wwohCybk8pVHNjO5IIMPnTaZmSXZSR5x8swry8FmwMMbqojGTG45ZwYXjbOKo4iICCi0iYicNMFIlM8+uJFnttZxypR8bjl7OndcMJs0pz1xTFluGr+6eUUSR5k60l12ZpZk8fLuRgAWVxx7HZuIiMhYpdAmInKCba/x8Me3DrOv0c+6XQ187bJ5fGzVtHHTVOR4LJyQy646H0VZbsq0QbaIiIxTCm0iIidQNGbyuT+9y/YaDzYD/vPy+Xx01bRkD2vUWDgxl79uPMLiilyFXBERGbcU2kREsNrr/279Qc6bV8K0okwAMlzD/1dkoy/IA+sPsbvey/YaD/fctJRz55Yc1zXHo4UTrSmRiyZqaqSIiIxf+u1BRMaNJl+QRzYeYU+9jwUTcznY6GfDoRY6QlEafSEafUF+8NwuDKAiP4On//WsIYesN/Y18ctX9vPy7kYCkSg2w+Cc2cVctqhclaJhWFyRy9WVE7iyckKyhyIiIpI0Cm0iMqaFozFe2FHP1iNt/OrVA/iCEbLcDh566zAuh43FE3OZkJdOcbab29cs5e0DzTT5Q/z61QN88+/bOX16IafPKKQoyw1Y3R83Hm7l9eoI5o56Kiflkel28Pz2Op7dVsejm45QnOXmulMm8s9nTmNaYSaGgQLbMKU57fzvDUuTPQwREZGkUmgTkVHnjX1N/OntKgqzXNx2zgzyM119Hren3svn/vQu71W1AbBmTjFfumQes0qyONzSTnluOi5Hzw2YT4vvieYPRvjDG4f4wxuHyHTZKc9Lx9MRJmZaUx8BfvbeWwBkuOy0h6Jkpzn4yOlT+feL52gapIiIiIwY/VbRj0g0xv2vH+T/1u5hUkEG/3zmNK5cYk3POdDoJ2qaVOSn43bYB7hS1/UeeOMQj206wodOm8K1yypO5PBFxqxozOTLj2ymti1AKBLjH9vqeP/yCvLSXeTF9zp7fW8TbR1hnt5aS6bLzo9uXMrqOcXkpHVtyDylMPOYn/P1KxawalYxZTlp/OGNg/iCUQozXQQiUc6dW4K/aifT5i1h0+FWDre0c+H8UlbNLMJhtx3zuiIiIiJDpdDWj9+81jU1qskf5LMPbuSPbx3CZhiJPYMyXHbOmlXE4oo8Nh1uJT/DyaKJuZw2vZBZpT03xP3u0zv4+cv7yXTZueuJbZw/v7THL5AiMjjPbq1lX4OfH9+4lPLcND7z4Ebufnpnj2Oy3A5y051cML+UO69YQHG2e8ifk+l2JP6iZuW0gl7vr23dzekzrKmTIiIiIieSQls/bjp1MhX5GVy0oJRozOTel/by6KZq2oMRPn/hbCbmp7PhYAvPb6/nma11TCpIxxeI8Ke3q7DbDO44fxahSIyXdjfS5AtS1dLBR06fwnWnVHDlPa/yw+d287XL5vW5zqWtI0xOmvV/TTgaIxoze2y+Oxz13gB2w6Awa+i/vIqkkp+/vI+phRlcuqgcu83g9S+fRyAcpbU9TGtHiGA4xrzynF7THkVERERGK4W2fmS4HFy8sAwAh93g0+fO4tPnzupxzDVLK7jrKpNmf4jCLDemaVLV0sE3/raV7z27C5sBlZPyWD4ln8sWlfP5i+bgtNt4/ykV/PKV/exv9POdaxdREt8wdnedl/9+ZifPbqtj6eQ8Wts62P/0U2S47Nz7oVM4e3bxoMZumiZtHWEeeOMQ963bR3luGrvrfUzKT+eZO84e9JROkVRT5wnwzqFWvnDRHOy2rr/wSHPaKcu1U5arzZdFRERk7FFoO05Gt+qVYRhMKsjgZx9ezntVrUwvyiI3o/cUyO9et5i55Tnc/fQOzv3+S0zIS8M0YW+DjwyXgw+fNoXnt9dBxOSz587k2W113PK7DXzpkrlcuqic6tYOntxcQzAS49plE1lckQfAwSY/P31xL8/vqKPRFwLg7NnFRGMxZpeW8/i71dz1xDY6QjHSnDZKc9Jo8Aap8wT4xNnTWTY5H7vNYGt1Gy/uqKckO43m9hArphZwypT8k3ZPRfrzwo56AM6fV5rkkYiIiIicPAptJ4DdZrB0cv8hx2Yz+NiqaayeU8xPX9xLeygCwAXzS/n4WdMpyHRx19ULWbt2LatXz+HDp0/l1t9v4OuPb+Xrj28FwGW3YbPB79cf5IaVkwB46M3DOOwGF84vY3FFLnPLclg1qyjxua0dYX6//hA5aQ7sNoOW9jAZLjvpTjvP3vs6hgE5aU7aOsI9x2vA1ZUTSXPZuXhBGWfNKlL78kGIxUy8wQiZLjvr9zXz9801zC3L5iNnTE320FJSZ4W41hPgSEsHVS0dLJ+az4IJXZsqP7+9jol56cwuzUriSEVEREROLoW2JJpRnMX3P7BkwOOKs908fOvpvFvVxjsHW3A6bFxdOYFYDO76+zYe3lBFOGpyw4pJ/Mt5sxLTLY/23+9bzNqd9VyxZAIZLgfB+Ma/4WiMxzZVU9PaQUt7mNIcNzesnIw/GCHdaefbT27n+R31RGMmf3jjEJctKuf7H1jS5zq7dbsa+P6zO2kPRblkYRm3rZ5JumtsT8c0TZNHNh7hpV0NuOw2DjT5cTls7Kn3UecJ4nbYCEZiAKQ77Vx3SgVZ7tT+Ry8SjREz6XddmGmaIxbcTdPk5d2NfPXRzRxu7uj1/uSCDCbkpVHnCXK4uZ0PnjpZf2kgIiIi40pq/+YoCYZhUDkpj8pJeT1e/977l3DXVQvpCEcp6Gevqk6lOWlcv2Jy4ufOtW1Ou40bV07udXznZsKdG9sGI1F++cp+7n56J7WeAP924Wze3N/MtKJMzpxZxENvHuL7/9jFtMJMJuan86MX9vB/L+1lVkk28yfkML88hysrJySuO5oFI9FEW/nfvn6QDQdbEh0KpxZm0B6KsqQijyWT8qj3BDhteiG56U5u+sUbPLm5hg8sn0QwEuWPbx1mW7WHstw0PnL61D73G9vX4MNpt1GRn35SwsrmqjY+8du3qfUEKMpyUZqTRlGWm3pvMPHdj7R0cNnicibmpXPu3BKWTs4nEI7yv8/tpsUfIjfDiacjHO+q6uJDp03hssXlmKbJ1moPm4+08fz2OvY1+GluD9HaHmZ6USZfu2we5bnplOWmUZLtZu2uBtbtaqDZH2LBhBxml2bxodOmnPB7ICIiIpJKFNrGgHSX/aRUs9wOO59aPZMpBZnc8adN3PTzN3odc1XlBP7ftYvIcDl460Azz2+vZ1uNh7U7G3h4QxW/enU/N506mU2HWnE77VROyuPapRP73Rx5JEVjJqZp0uALUpzlPuZ+WqZpcri5A4fd4G/vVvOb1w6Ql+FiYl4aE/PSeX1fE7vqfACU56bxnWsX8YHlk7DZ+g9VpmkyvSiT371+kEA4ys9e2seR1g4KM120tIf4+bp95GW4qJyUx8ppBeyo9bDxUCs7ar0ATC/O5KyZRbyws57PrJnFB1ZMSly7urWDSNSkPC8N5wD7hEWiMTrCUTJcDmwGbKyP0LbpCN5AhOe31/HqniaKs938y3mzqPcGqG0L0OgLUZbjxmYYGIbBKZPz+fvmGtpDUX796gF+//FT+dHzu3lhRz0l2W68gQgOm8GyKflUtbRz+x/e4XfrC/AGImyt9gAwITeNpVPyyUlzsHRyPlcumdCrevvh06bwYYU0ERERGecU2mTILltcTkV+OhsPtXBV5UQOt7Tzj211TCnM5LplExPVoBVTC1gxtWt/q42HWvjob97i7qd3Mr0ok2Akxt/erebRjUf4862nH/e2Bp3qvQE8HRFmlljrnto6wvzX37bx141VmKZ1zKSCdFbNLOJgUzs1bQEWTsxlWlEmNa0dbD7SRrM/lKgsAZw+vZAMl52qlg7e2N9MbrqTn9y0jMkFGcwpyx5Ue3nDMLhx5WS+9eR2Nh9pY3FFLt+9bjFnzixkd72PX72yn/ZQlLU76/n75hryMpwsmJDDf14+H4fd4C/vHOG36w8yITedLz+ymY5wlEkF6Ty8oYonN9cCkJfhZOXUAkzgX8+fxezSbIKRGA6bwdZqD79ff5Dnt9fhCVhTX6cUZrCjNgjvbAKsqYgfOm0Kt62eMeDeZt+9bjHVbR1c9qNXuPonrwLw7WsWcdOpVtW2cwplOBrjxy/s4cUd9dgMg29ds5AzZhQxpSDjmCFXRERERCwKbTIsSyZZU/8A8jNdiQ6Wx7J0cj5P/cvZ+IJhZpZYm48/s7WWW363gdsfeIfbVs+gJDuNyYUZQxpLLGayvdZDOGryP28H2PzM85j/v727j5WrrvM4/v72tre0vX2A20da6AMUaSugtELFBx4MdoEE1MJSIkg3y7q6PuzGbLIYzYZsYlSMigZMFoQNmA3dLD6sZKNRKjcBBVwrhbWyfWJRat1urYC0ZVtu+9s/5tw6XG/be+bOufOb8f1KTu7Mb878zjmfnOmv3zkPk2DlopPYumsve/YdJALee/6pTO8Zz9QJ4/jWxp1852f/w8Lpk1g8s4fHn93Dg0/tZOqEcZx76jSWnTyVc06ZSkq1QujKc05+zamJjV7TddPbFvLOZbM42H+Y02f2HOnjjFmT+czqswHYf7Cfl155ldlTTnjNMm5YOZ8D/bXf7bv+7ieO3JRm8vixfPji0zm1dyKPbP0Nm3a+xIv7X+U9X/kRXWOC/QcPHelj8vjaT1ksntXDL/bsZ+PzL3L9km6uX3U+Y8eM4bQZk4a9XWPGBPNOnMg/3rCch36+i9XL57FkzpQjrw/0M65rDB+79Aw+dukZpfOSJEmSRZtGWe13tH5/o5RVy2bzicuX8Pnvb2Z9cTv36847lT9/60IiajdrOZoD/Yd46vmXuO2hLfxo+x4AJoyFj16ymAR8fcMOLjh9OkvmTGblol7Orbuj55+9ZeEf9Hf4cCKCYRUtjV5bFhHM7510zHkmdo9lYvcffjQj4sjRyG988AI27fwdLxQ/yTDQ/qcraqdM7n75ALd+97+Y0N3F3GkT6D+cmDttAu9YMpPJJ7z2Zyj6+vo4c/YUGrVyUS8rF/U2/H5JkiQdm0WbWu4v3r6Ia1bM44n//i2PP7uHf/rhc9z/418CsHhmD6fP7OFAf+235RZOn8SbFpzE0jlTWHPX4zy7ex8Tu7v45BVL6O3pJnZt4V3FEZ2yR3ba6VS9iOD1c6ce9fUZk8fzuWuOf2dSSZIk5c+iTVmYNrGbVctms2rZbC44bTov7DvI/oP9PLx5N5t3vcyEcV288uohvrdpF3c8vJ1xXUHXmOC2a9/AhWfMOHIjk76+bS3eEkmSJKm5LNqUnUuXzjryeO2g0xhfKW7U8cCGHax9ywLetnjGaK+eJEmSNKos2tRWJnR3cdlZc7jsrDmtXhVJkiRpVBz/PuWSJEmSpJaxaJMkSZKkjFm0SZIkSVLGLNokSZIkKWMWbZIkSZKUMYs2SZIkScqYRZskSZIkZcyiTZIkSZIyZtEmSZIkSRmzaJMkSZKkjFm0SZIkSVLGKivaIuJPImJzRGyLiJurWo4kSZIkdbJKiraI6ALuAC4DlgLXRcTSKpYlSZIkSZ2sqiNt5wHbUkrPppQOAuuAqypaliRJkiR1rKqKtrnA83XPdxRtkiRJkqQSIqXU/E4jrgFWpZRuKp7fAJyXUvpI3TzvB94PMGvWrOXr1q1r+nq0u71799LT09Pq1WgrZlaemTXG3Mozs3LMqxzzKs/MyjOzxpjb8Fx88cUbUkorhnptbEXL3AGcUvd8HrCzfoaU0p3AnQArVqxIF110UUWr0r76+vowl3LMrDwza4y5lWdm5ZhXOeZVnpmVZ2aNMbeRq+r0yP8AFkfEwojoBtYA365oWZIkSZLUsSo5PRIgIi4HbgO6gHtSSp86xry7gV9UsiLtbTrwm1avRJsxs/LMrDHmVp6ZlWNe5ZhXeWZWnpk1xtyGZ35KacZQL1RWtGnkIuInRzuvVUMzs/LMrDHmVp6ZlWNe5ZhXeWZWnpk1xtxGrrIf15YkSZIkjZxFmyRJkiRlzKItb3e2egXakJmVZ2aNMbfyzKwc8yrHvMozs/LMrDHmNkJe0yZJkiRJGfNImyRJkiRlzKKtiSLilIh4OCKeiYhNEfHXRftJEfH9iNha/D2xaO8t5t8bEbcP6uvaiHi66OfWYyxzeUT8Z0Rsi4gvR0QU7R8o2jdGxKMRsbTKbW9UZpmtjYjdRWYbI+KmKre9EZnl9cW6rLZExIsVbvqIZJbb/IhYX/TRFxHzqtz2RrUos09FxPMRsXdQ+9sj4qcR0R8RV1exvc3QQGaXRsSGYj/ZEBGX1PU15P4zxDKPtp9ln1lmeXXqmFllZp04ZlaZV9uMmZBddm0xblYupeTUpAmYA5xbPJ4MbAGWArcCNxftNwOfLR5PAt4KfAC4va6fXuCXwIzi+b3AO46yzB8DbwYC+A5wWdE+pW6eK4HvtjqfNshsbX2fOU455TVono9Q+z3GlmeUe27AvwI3Fo8vAb7W6nwyymxlsdy9g9oXAGcD9wFXtzqbJmb2RuDk4vHrgV8db/8psZ9ln1lmeXXqmFllZmvpvDGzsrwGzZP1mJlbdrTJuFn15JG2Jkop/Tql9NPi8cvAM8Bc4Cpq/1Gh+PuuYp59KaVHgf8b1NUiYEtKaXfx/CFg9eDlRcQcagPNY6m2J99X1/fv6madBGR58WJOmbWDjPO6Dri/8S2rVma5LQXWF48fLtYhO6OdWdHH4ymlXw/R/lxK6Wng8Ig2qmINZPZkSmln0b4JOCEixg/3c3ecMSD7zDLLq1PHzMoyawcZ55X1mAnZZdcW42bVLNoqEhELqH3r8AQwa+A/IsXfmcd5+zbgzIhYEBFjqe20pwwx31xgR93zHUXbwDp8KCK2U/tW5KONbcnoySEzYHVx+P2BiBjq/dnIJC8iYj6wEPhB+a0YfRnk9hS/L1reDUyOiN7yWzJ6RimzjtJAZquBJ1NKBxjG564w3Pmyl0NefwRjZhX7WCePmZV8JtttzIQssmu7cbMKFm0ViIge4OvA3wz69m5YUkovAB8E/gV4BHgO6B9qUUO9va6fO1JKpwF/B3yy7HqMpkwyexBYkFI6m9rRgHuHmDcLmeQ1YA3wQErpUNn1GG2Z5Pa3wIUR8SRwIfCro/SRhVHMrGOUzSwilgGfBf5yoGmI2YY68jPc+bKWS16dPGZWlFnHjpkVfybbZsyEbLJrq3GzKhZtTRYR46jt3P+cUvpG0byrOOw7cPj3f4/XT0rpwZTS+SmlNwObga0R0VV3Ees/UPsWov5izHnAziG6W0fGpzPkkllKaU/xrRDAXcDyZmxfs+WSV501ZH6aB+STW0ppZ0rpPSmlNwKfKNpeatJmNtUoZ9YRymZWXFD/TeB9KaXtRfOQ+88IxoBsZZpXR42ZVWXWqWPmKOxjbTFmQj7ZtdO4WSWLtiaKiADuBp5JKX2h7qVvAzcWj28E/m0Yfc0s/p4I/BXw1ZTSoZTSG4rp74vD0i9HxMpi2e8b6DsiFtd1dwWwdYSbV4nMMptT192V1M7fzkpOeRXvfR1wIvBYEzavMjnlFhHTI2Lg396PA/c0YRObbrQza+7at0bZzCJiGvDvwMdTSj8cmPlo+0/Zz2fucsqrU8fMijPruDGz6s9ku4yZkFd27TJuVi5lcDeUTpmo3TktAU8DG4vpcmp3T1tPbRBYD5xU957ngN8Ce6l9y7C0aL8f+HkxrTnGMlcAPwO2A7fDkR9M/xK1C0E3Urtoc1mr82mDzD5dZPZUkdmZrc4n57yK124BPtPqXNopN+DqYnlbgK8C41udT0aZ3Vq873Dx95ai/U3F833AHmBTq/NpRmbUTsHbVzfvRmDmsfafEvtZ9plllldHjpkVZ9ZxY2aVeRWv3UIbjJm5ZUebjJtVTwNhSJIkSZIy5OmRkiRJkpQxizZJkiRJyphFmyRJkiRlzKJNkiRJkjJm0SZJkiRJGbNokyRJkqSMWbRJkiRJUsYs2iRJkiQpY/8P57AB0KPtEg4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train,label='Training Data')\n",
    "plt.plot(test,label='Test Data')\n",
    "plt.plot(test.index,ARIMA_predictions,label='Predicted Data - ARIMA')\n",
    "plt.legend(loc='best')\n",
    "plt.grid();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55.77109139840425\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import  mean_squared_error\n",
    "rmse = mean_squared_error(test['Avg spending'],ARIMA_predictions, squared=False)\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.556558967537567\n"
     ]
    }
   ],
   "source": [
    "def MAPE(y_true, y_pred):\n",
    "    return np.mean((np.abs(y_true-y_pred))/(y_true))*100\n",
    "\n",
    "mape=MAPE(test['Avg spending'].values,ARIMA_predictions[0])\n",
    "print(mape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Those Model MAPE is less that 10%. Model is not capturing the trend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Improvement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Will use Exponential Smoothing model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ExponentialSmoothing Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Avg spending</td>     <th>  No. Observations:  </th>        <td>544</td>      \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ExponentialSmoothing</td> <th>  SSE                </th>    <td>128328.668</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Optimized:</th>                <td>True</td>         <th>  AIC                </th>     <td>2994.090</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Trend:</th>                  <td>Additive</td>       <th>  BIC                </th>     <td>3041.378</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seasonal:</th>               <td>Additive</td>       <th>  AICC               </th>     <td>2994.777</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seasonal Periods:</th>           <td>7</td>          <th>  Date:              </th> <td>Sun, 01 Sep 2024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Box-Cox:</th>                  <td>False</td>        <th>  Time:              </th>     <td>12:09:18</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Box-Cox Coeff.:</th>           <td>None</td>         <th>                     </th>         <td> </td>       \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>                  <th>coeff</th>                <th>code</th>               <th>optimized</th>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smoothing_level</th>    <td>           0.9263471</td> <td>               alpha</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smoothing_trend</th>    <td>           0.0003019</td> <td>                beta</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smoothing_seasonal</th> <td>           0.0001550</td> <td>               gamma</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_level</th>      <td>           62.911099</td> <td>                 l.0</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_trend</th>      <td>           1.1430887</td> <td>                 b.0</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.0</th>  <td>          -0.8912932</td> <td>                 s.0</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.1</th>  <td>          -1.5465241</td> <td>                 s.1</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.2</th>  <td>          -1.1543896</td> <td>                 s.2</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.3</th>  <td>           0.6775944</td> <td>                 s.3</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.4</th>  <td>           2.3788025</td> <td>                 s.4</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.5</th>  <td>           0.1740374</td> <td>                 s.5</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.6</th>  <td>           0.3270379</td> <td>                 s.6</td> <td>                True</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                       ExponentialSmoothing Model Results                       \n",
       "================================================================================\n",
       "Dep. Variable:             Avg spending   No. Observations:                  544\n",
       "Model:             ExponentialSmoothing   SSE                         128328.668\n",
       "Optimized:                         True   AIC                           2994.090\n",
       "Trend:                         Additive   BIC                           3041.378\n",
       "Seasonal:                      Additive   AICC                          2994.777\n",
       "Seasonal Periods:                     7   Date:                 Sun, 01 Sep 2024\n",
       "Box-Cox:                          False   Time:                         12:09:18\n",
       "Box-Cox Coeff.:                    None                                         \n",
       "=================================================================================\n",
       "                          coeff                 code              optimized      \n",
       "---------------------------------------------------------------------------------\n",
       "smoothing_level               0.9263471                alpha                 True\n",
       "smoothing_trend               0.0003019                 beta                 True\n",
       "smoothing_seasonal            0.0001550                gamma                 True\n",
       "initial_level                 62.911099                  l.0                 True\n",
       "initial_trend                 1.1430887                  b.0                 True\n",
       "initial_seasons.0            -0.8912932                  s.0                 True\n",
       "initial_seasons.1            -1.5465241                  s.1                 True\n",
       "initial_seasons.2            -1.1543896                  s.2                 True\n",
       "initial_seasons.3             0.6775944                  s.3                 True\n",
       "initial_seasons.4             2.3788025                  s.4                 True\n",
       "initial_seasons.5             0.1740374                  s.5                 True\n",
       "initial_seasons.6             0.3270379                  s.6                 True\n",
       "---------------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.api                  import  ExponentialSmoothing\n",
    "\n",
    "model_TES = ExponentialSmoothing(train,trend='additive',seasonal='additive',initialization_method='estimated')\n",
    "model_TES = model_TES.fit(optimized=True)\n",
    "model_TES.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "TES_predictions =  model_TES.forecast(len(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train,label='Training Data')\n",
    "plt.plot(test,label='Test Data')\n",
    "plt.plot(test.index,TES_predictions,label='Predicted Data - TES')\n",
    "plt.legend(loc='best')\n",
    "plt.grid();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83.22313203237533\n"
     ]
    }
   ],
   "source": [
    "rmse = mean_squared_error(test['Avg spending'],TES_predictions.values, squared=False)\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.554653417365722\n"
     ]
    }
   ],
   "source": [
    "mape=MAPE(test['Avg spending'].values,TES_predictions.values)\n",
    "print(mape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\malli\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ExponentialSmoothing Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Avg spending</td>     <th>  No. Observations:  </th>        <td>544</td>      \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ExponentialSmoothing</td> <th>  SSE                </th>    <td>128328.668</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Optimized:</th>                <td>True</td>         <th>  AIC                </th>     <td>2994.090</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Trend:</th>                  <td>Additive</td>       <th>  BIC                </th>     <td>3041.378</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seasonal:</th>               <td>Additive</td>       <th>  AICC               </th>     <td>2994.777</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seasonal Periods:</th>           <td>7</td>          <th>  Date:              </th> <td>Sun, 01 Sep 2024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Box-Cox:</th>                  <td>False</td>        <th>  Time:              </th>     <td>12:10:40</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Box-Cox Coeff.:</th>           <td>None</td>         <th>                     </th>         <td> </td>       \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>                  <th>coeff</th>                <th>code</th>               <th>optimized</th>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smoothing_level</th>    <td>           0.9263471</td> <td>               alpha</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smoothing_trend</th>    <td>           0.0003019</td> <td>                beta</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smoothing_seasonal</th> <td>           0.0001550</td> <td>               gamma</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_level</th>      <td>           62.911099</td> <td>                 l.0</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_trend</th>      <td>           1.1430887</td> <td>                 b.0</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.0</th>  <td>          -0.8912932</td> <td>                 s.0</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.1</th>  <td>          -1.5465241</td> <td>                 s.1</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.2</th>  <td>          -1.1543896</td> <td>                 s.2</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.3</th>  <td>           0.6775944</td> <td>                 s.3</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.4</th>  <td>           2.3788025</td> <td>                 s.4</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.5</th>  <td>           0.1740374</td> <td>                 s.5</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.6</th>  <td>           0.3270379</td> <td>                 s.6</td> <td>                True</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                       ExponentialSmoothing Model Results                       \n",
       "================================================================================\n",
       "Dep. Variable:             Avg spending   No. Observations:                  544\n",
       "Model:             ExponentialSmoothing   SSE                         128328.668\n",
       "Optimized:                         True   AIC                           2994.090\n",
       "Trend:                         Additive   BIC                           3041.378\n",
       "Seasonal:                      Additive   AICC                          2994.777\n",
       "Seasonal Periods:                     7   Date:                 Sun, 01 Sep 2024\n",
       "Box-Cox:                          False   Time:                         12:10:40\n",
       "Box-Cox Coeff.:                    None                                         \n",
       "=================================================================================\n",
       "                          coeff                 code              optimized      \n",
       "---------------------------------------------------------------------------------\n",
       "smoothing_level               0.9263471                alpha                 True\n",
       "smoothing_trend               0.0003019                 beta                 True\n",
       "smoothing_seasonal            0.0001550                gamma                 True\n",
       "initial_level                 62.911099                  l.0                 True\n",
       "initial_trend                 1.1430887                  b.0                 True\n",
       "initial_seasons.0            -0.8912932                  s.0                 True\n",
       "initial_seasons.1            -1.5465241                  s.1                 True\n",
       "initial_seasons.2            -1.1543896                  s.2                 True\n",
       "initial_seasons.3             0.6775944                  s.3                 True\n",
       "initial_seasons.4             2.3788025                  s.4                 True\n",
       "initial_seasons.5             0.1740374                  s.5                 True\n",
       "initial_seasons.6             0.3270379                  s.6                 True\n",
       "---------------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_TES = ExponentialSmoothing(train,trend='additive',seasonal='additive',initialization_method='estimated')\n",
    "model_TES = model_TES.fit(optimized=True)\n",
    "model_TES.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "TES_predictions =  model_TES.forecast(len(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train,label='Training Data')\n",
    "plt.plot(test,label='Test Data')\n",
    "plt.plot(test.index,TES_predictions,label='Predicted Data - TES')\n",
    "plt.legend(loc='best')\n",
    "plt.grid();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83.22313203237533\n"
     ]
    }
   ],
   "source": [
    "rmse = mean_squared_error(test['Avg spending'],TES_predictions.values, squared=False)\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.554653417365722\n"
     ]
    }
   ],
   "source": [
    "mape=MAPE(test['Avg spending'].values,TES_predictions.values)\n",
    "print(mape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Residual Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "residuals = test['Avg spending'].values-TES_predictions.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4Ifz1r+x5YjcumnsCC6ngGMZRxTT+Mu93/Pocg6C0OGcGJUmSVLpefRWGDEmng86ezZPsw2D6cR97AF8tA82iAbxU7JwZlCRJUmmJER5+GHr2hE02gUsugSOPhClTOK3b7dzHniwaBCGbBvBSsTMMSpIkqU3U1kJVFVRUpNva2ma+wLx5cPXVsP328OMfw6OPwv/+b5r2GzsWNt+8qBrAS8XOZaKSJEkquNpa6NUL6uvTdV1duoY8+vPNmpXC3vDhKfhtvHHqFXj00d9Ifg2vZQN4aelCjDHrGgqquro6Tpo0KesyJEmSylpVVQqAi+vWDaZNa+Kbpk+HYcPg4ovhs8/SbGD//rDffml6UVJeQgiTY4zVi487MyhJkqSCa+oAl0bHn3wSBg2C665L14cemkLgdtsVrD6pHBkGJUmSVHCVlY3PDP73YJcFC+DWW1MIfOQRWHll6NsXevf29BepQJxflyRJUsE1dbDL386uhwsvhE03hQMOSFOFgwfDm2/CeecZBKUCcmZQkiRJBbf4wS7brfcul213AVv8ehR89FE6IfSaa+Cgg2A5/4oqtQU/aZIkSWoTNTVQs9WUtBT0yivh5nmpV2D//rDLLhDC0l9EUqsxDEqSJKmwYoR77kkh8O67oUsXOOkk6NMHevTIujqpbBkGJUmSVBhz5qQZwMGDYcoU+Pa30+bBk0+Gb30r6+qksmcYlCRJUuv68MPUFP6CC+Ddd2HLLeGyy+CII6BTp6yrk5RjGJQkSVLrePVVGDIELr8cZs+GvfaC8eNhzz3dDygVIcOgJEmSll2M8K9/pf2AN98MHTvCUUelHoFbbJF1dZKWwDAoSZKk5ps/H667LoXASZPSHsABA+D009PeQElFz6bzkiRJ+q/aWqiqgoqKdFtbu9gTPv00HQiz0UZpD+CsWTBqVGoe+Je/GASlEuLMoCRJkoAU/Hr1gvr6dF1Xl64Ban44HYYPh4svToHwRz+CESNg//1TcpRUcgyDkiRJAtIqz4Yg2OC79ZNYqdcgmPOPNHDoodCvH1RXt32BklqVYVCSJElAWukJEFjI/txKfwbxYx5mVv3K0L8PnHkmVFZmWqOk1uOcviRJkgDYeP16TmEUL7EpN9OTKqbRl8HsssGbcP75BkGpnXFmUJIkqdy9+y6MHMkzH4+iMx/yBN/nMK7meg6mU9flGHNO1gVKKgTDoCRJUrmaMiWdDFpbC/Pm0blnT+7esj+9xu3C9DcDlZUwcCDU1GRdqKRCMAxKkiSVkxjh3ntTf8C77oIuXeCkk6BPH+jRg58C0/6cdZGS2oJhUJIkqRzMmQNXX51mAp97LvUDHDgQTj45NYyXVHYMg5IkSe3ZRx/BRRfBBRfAO+/AllvCZZelhvGdOmVdnaQMGQYlSZLao9deg6FDU/Crr4e99oJx42DPPSGErKuTVAQMg5IkSe1FjPDoo2k/4E03QceO6fSXfv1giy2yrk5SkTEMSpIklbr58+H669N+wCeegNVXh//5HzjjjLQ3UJIaYRiUJEkqVZ9+CpdcAsOGQV0d9OgBF14Ixx4LXbtmXZ2kIleRdQGSJEnlorYWqqqgoiLd1tYu4wu9+Sb8+tfM/fYG0K8fD9dV8ss1b6T2/70Ep55qEJSUF2cGJUmS2kBtLfTqlc5ygTSR16tXup93U/fJk9N+wGuvZWGEm8IhnEs/JvF9eB+uPAWosEm8pPyEGGPWNRRUdXV1nDRpUtZlSJKkMldVlQLg4rp1g2nTlvCNCxfCrbem/YAPPQQrrQS//CW7XHMmj73VrfmvJ6nshBAmxxirFx93magkSVIbmD69eePU16f+gN/9LvTsCVOnplnBGTNg0CAef/ubQXCJrydJizEMSpIkLUFr7fOrrMxz/L334Oyz0wOnngorrwxXXQWvv55aRKy8cvNeT5KaYBiUJElqQsM+v7q61MKvYZ/fsgTCgQO/ea5L165pHIDnn4cTT0xp7q9/hR/8AB5+OLWKOPxwWG655r2eJC2FYVCSJKkJAwZ8deBLg/r6NN5cNTUwZkza0xdCuh0zOlKz1j2wzz6pKfxVV6VA+NJLcOON8MMfpifn+3pjPDxGUv48QEaSJKkJFRVpRnBxIaRzXZbZ3Lkp+A0eDM89B2uvDb17wymnwLe+1YIXlqRvauoAGVtLSJIkNaGysvETQJd5X95HH8Ho0TBiBLzzTpoNvPRSOPJI6NSpRbVKUnO5TFSSJKkJrbYv77XX4IwzYIMN4H/+B7bcEu66K80KHn+8QVBSJpwZlCRJakLD/rsBA1LLhsrKFATz2pcXIzz6aFoKeuON6QCYmpp0IuiWWxaybEnKi2FQkiRpCWpqmnkoy/z58M9/pp6ATzwBq6+eZgNPPx3WWadgdUpScxkGJUmSWsNnn8HYsTBsWNpo+J3vwMiRcOyxsMIKWVcnSd9gGJQkSWqJN9+E4cNTX4dPP03tIIYOhZ/9DDp0yLo6SWqSYVCSJGlZTJ6c9gNee23aH/iLX6T9gNtvn3VlkpQXTxOVJEntVm0tVFWlfoFVVem6RRYuhFtugV13herqdL93b3j9dbj6aoOgpJLizKAkSWqXamuhVy+or0/XdXXpGpp5IAykF7niChgyBF5+ObWIOP98OOkkWGWVVq1bktqKM4OSJKldGjDgqyDYoL4+jeftvffg7LNTT4lTToEVV4Qrr0wzgf37GwQllTRnBiVJUrs0fXrzxr/mhRfSfsAJE2Du3HQYTL9+8KMfQQitWqckZcUwKEmS2qXKyrQ0tLHxRsUI992XQuAdd0DnznD88dC3L2y8cUFrlaQsuExUkiS1SwMHQteuXx/r2jWNf83cuTB+PGyzDfzkJ+mU0D//ObWMGDXKICip3XJmUJIktUsNh8QMGJCWhlZWpiD438NjPv4YRo+GESPg7bdh883hkkvgyCPTrKAktXPODEqSpKLUGm0hampg2rTUEWLatFwQfP311A5i/fXh979PIfCOO+A//4ETTjAISiobhkFJktTqWhrkGtpC1NWlrXwNbSFa1Cfwscfg4IOhR480I3jIIfDss3D33bD33h4MI6nshBhj1jUUVHV1dZw0aVLWZUiSVDYW7+8Haa/emDH59/erqmr88Jdu3dIMX94WLIAbboBBg2DiRFhtNTj1VDj9dFh33Wa8kCSVrhDC5Bhj9TfGDYOSJKk1tUaQq6hIM4KLCyEt+Vyqzz6DSy+FYcNg6lTYaKN0Kuhxx8EKK+RXhCS1E02FQQ+QkSRJrapF/f1ymt0WosGMGelAmNGjYdYs2GWXNCv4859Dhw75FyBJZcA9g5IkqVU1FdiWGuQWkXdbiAZPPw1HHQXdu8P558Nee6Vlof/6Fxx4oEFQkhphGJQkSa2q2UGuETU1aY9ht25paWi3bo3sOVy4EG69FXbbDbbdFm66Cc44I50Wes01sMMOrfLrkaT2qmiXiYYQpgGfAQuA+THG6hDC6sA1QBUwDTg0xvhxVjVKkqRvWmp/v2a8TqPfM3s2XHEFDBkCL72UWkScdx6cdBKsumpLy5ekslHsM4O7xRi3XmSz4++A+2KMPYD7cteSJKmVtbQ1RKP9/Vpq5kz4wx9Sujz55DTdWFsLb7wBv/qVQVCSmqnYw+DiegLjcvfHAQdkV4okScWrJWGuID3+WuKFF9KsX2Ul/PnPsNNO8MADMGkSHHkkdOyYUWGSVNqKtrVECGEq8DEQgdExxjEhhE9ijKsu8pyPY4yrLel1bC0hSSo3Le3z12o9/loiRrj//nQS6B13QOfOcOyxqT3EJpu0URGS1D6UXJ/BEMK6Mca3QwhrAfcAvYGb8wmDIYReQC+AysrK7eoa+xNNkqR2qqVhrsU9/lpi7ly4+moYPBiefRbWWis1iD/1VFhzzQL/cElqn5oKg0W7TDTG+HbudiZwA7A98F4IYR2A3O3MJr53TIyxOsZYvaZ/cEiSykxL+/y1RmuIZvv4Y/jb31JriGOPhXnzYOzYlGrPPtsgKEkFUJRhMISwQghhpYb7wE+BKcDNwLG5px0L3JRNhZIkFa+WhrnWaA2Rt9dfh96904mgv/89bLYZ3H47TJkCJ56YlodKkgqiKMMgsDbwrxDCs8ATwG0xxjuBvwE/CSG8Cvwkdy1JkhbR0jCXV4+/lnrsMTj4YOjRA0aPhl/8Ap55Bu65B/bZJ/1gSVJBFe2ewdbiATKSpHJUW9vyPn+tbv58uPHGdCjMxImw2mpwyimpUfy662ZcnCS1X03tGSzapvOSJGnZNdmwPQuffQaXXgrDhsHUqbDhhjBiBBx3HKy4YtbVSVLZMgxKkqTCmDEjhb7Ro2HWLNh5Zzj/fOjZEzp0yLo6SSp7hkFJktS6nn46LQW95prUi+Lgg6FfP9hxx6wrkyQtwjAoSZJabuHC1Bx+0CB44IG0/PP00+Gss1K7CElS0SnW00QlSSprtbWpeXxFRbqtrc26oibMng0XXwybbw777w+vvALnngtvvglDhxoEJamIOTMoSVKRqa2FXr2gvj5d19WlayiiQ2FmzoQLL0xf778P22wDEybAoYdCx45ZVydJysNSZwZDCBuFEDrl7u8aQjgzhLBqwSuTJKlMDRjwVRBsUF+fxjP34ospmVZWwp/+BDvskJaFTp6ckqpBUJJKRj7LRK8HFoQQvgNcAnQHrixoVZIklbHp05s3XnAxwv33w377wWabwRVXwLHHpmB4yy2w6642iZekEpRPGFwYY5wPHAgMjTH2BdYpbFmSJJWvysrmjRfMvHlp6ed228Eee8CTT6bZwOnTU7uITTdt44IkSa0pnzA4L4RwBHAscGtuzDUgkiQVyMCB0LXr18e6dk3jbeKTT9IhMN27w9FHw5dfpkNipk+Hs8+GNddso0IkSYWUTxg8HtgJGBhjnBpC6A5MKGxZkiSVr5oaGDMGunVLqy+7dUvXBT88ZurU1Api/fXht79NM3+33QZTpsBJJ0HnzgUuQJLUlkKMcelPCqELUBljfLnwJbWu6urqOGnSpKzLkCSpeE2cmPoD/vOfqZfFEUekJvFbb511ZZKkVhBCmBxjrF58PJ/TRH8GPAPcmbveOoRwc6tXKEmS2s6CBXD99bDzzrDTTnDvvfCb38C0aTB+vEFQkspAPn0G/whsDzwIEGN8JrdUVJIklZrPP4fLLksN4d94AzbcEIYPh+OPhxVXzLo6SVIbyicMzo8xzgpfPzJ66WtLJUlS8XjrLRgxIp0C+sknaUbwvPOgZ0/o0CHr6iRJGcjnAJkpIYQjgQ4hhB4hhBHAYwWuS5KkklZbC1VVaQteVVW6zsSzz8Ixx6STQc87D/bcEx5/HB59FA46yCAoSWUsnzDYG9gcmANcBXwK9ClgTZIklbTaWujVC+rqUr/2urp03WaBcOFCuOOOFPy23jodDHPaafDaa/CPf8COO7ZRIZKkYpbXaaKlzNNEJUltraoqBcDFdeuWzmcpmC+/TE3iBw+GF1+E9daDM89MSXTVVQv4gyVJxayp00Sb3DMYQriFJewNjDH+vJVqkySpXZk+vXnjLfb++3DhhTByZLq/9dZwxRVw6KGw/PIF+qGSpFK3pANkzm+zKiRJKjK1tTBgQApwlZUwcGD+Td8rKxufGaysbN0aeeklGDIktYL48kvYbz/o3x923TV1q5ckaQma3DMYY3xoSV9tWaQkSctiWQ9xaemev4EDoWvXr4917ZrGWyxGeOAB2H9/+O53Ydw4OPpoeOEFuPVW2G03g6AkKS9NhsEQwrW52/+EEJ5b/KvtSpQklauWnMjZkkA3YADU1399rL4+jeejpgbGjEl7BENIt2PG5D+z2Kh581Lx220Hu+8OTzwBf/xjmrocMyYFQ0mSmqHJA2RCCOvEGN8JIXRr7PEYYyMLYIqPB8hIUmlqCHOLhrKuXfMPVS05xKWiIgXIxYWQDupsU598kn7Rw4enXoGbbgr9+sFRR0GXLm1cjCSpFDV1gMySlom+k7t7WoyxbtEv4LRCFSpJErR8dq4lh7g0tbev1ff8LcnUqdCnD2ywAfz2t7DJJnDbbfD88/DLXxoEJUktlk+fwZ80MrZPaxciSdKiWnoiZ0sCXUH3/C3NxIlwyCHwne+k00EPOACeegruuw/23TdNW0qS1AqWtGfw1BDCf4BNFtsvOBVwz6AkqaBaOjvXkkBXkD1/S7JgQWoMv8susNNOcM898KtfpdnBK66AbbYp0A+WJJWzJbWWuBK4AzgH+N0i45/FGD8qaFWSpLI3cGDjewbznZ1rCG7L2h6ipqaA4a/B55/DZZfB0KHwxhvQvTsMGwYnnAArrljgHy5JKndNHiDztSeF0AFYm0XCY4yxUK1zW5UHyEhS6WpJr7+i9tZbcMEFcNFF6YCYHXdM/QEPPBA6dMi6OklSO9PUATJLmhls+MYzgD8C7wENZ6hF4HutWaAkSYtrk9m5tvTsszBoEFx9dVoaeuCBKQTutFPWlUmSytBSwyDQB9gkxvhhgWuRJKn9WbgQ7rwTBg9Oh8CssAKccko6KXTDDbOuTpJUxvIJg28CswpdiCRJ7cqXX8KECSkEvvgirLsu/O1vaSPkaqtlXZ0kSXm1lngDeDCE8PsQQr+Gr0IXJkkqbbW1qfF7RUW6ra3NuqI28v778Oc/pyNIf/lLWH55GD8+nQz6298aBCVJRSOfmcHpua/lc1+SJC1Rbe3XTwKtq0vX0M72AC7q5ZfTLOD48WlWcN99037A3XZL/SkkSSoyeZ0mWso8TVSS2l5VVQqAi+vWDaZNa+tqCihGeOihdCjMrbdCp05w9NHQty9stlnW1UmSBLTsNNE1gd8AmwOdG8ZjjLu3aoWSpHZjehPNh5oaLznz5sG116aZwKeegjXWgD/8AU47DdZaK+vqJEnKSz57BmuBl4DuwJ+AacCTBaxJklQEWrLnr7KyeeMl45NP4Lzz0imgRx0FX3wBo0enlPvHPxoEJUklJZ8w+K0Y4yXAvBjjQzHGE4AdC1yXJClDDXv+6urSSsiGPX/5BsKBA6Fr16+Pde2axkvStGlp6ecGG8BvfgM9esAtt8ALL6TfmC5dsq5QkqRmyycMzsvdvhNC2C+EsA2wfgFrkiRlbMCArw5/aVBfn8bzUVMDY8akPYIhpNsxY0rw8Jh//xsOPRQ22gguuAB69oTJk+H++2H//dO0qSRJJWqpB8iEEPYHHgE2AEYAKwN/ijHeXPjyWs4DZCSp+Soq0ozg4kJIPdTbtQUL4Kab0n7ARx+FVVZJs39nngnr+2+hkqTSs8wHyMQYb83dnQXs1tqFSZKKT2Vl46eBlvyevyX54gu47DIYOhRefz1tlBw6FE44AVZaKePiJElqffmcJnoZ8I1/H87tHZQktUMDB369TyCU+J6/JXn77bQE9KKL4OOPYYcd4Jxz4MADYbl82vFKklSa8vlT7tZF7ncGDgTeLkw5kqRi0LC3b8CAdFBmZWUKgiW3529Jnn02LQW96iqYPz+Fv/79Yeeds65MkqQ20eym8yGECuDeUukz6J5BSdJ/xQh33plC4L33wgorpGWgZ52VDomRJKkdWuY9g43oAbTnXSOSpPbmyy9TX4zBg1M7iHXXTUtBTz4ZVlst6+okScpEPnsGPyPtGQy523eB3xa4LkmSWu6DD2DUqLQncOZM2GorGD8eDjsMll8+6+okScpUPqeJeoSaJKm0vPwyDBkC48alWcF99kn7AXffPfXHkCRJSw6DIYQuQA2wWW5oEnBdjHFuoQuTJKlZYoSHHkpLQW+5BTp1gqOPhr59YbPNlv79kiSVmYqmHgghbAm8CPwQmAbUAXsBj4YQVg0h/LVNKpQkNVttbWqTV1GRbmtrs66ogObNgyuvhOpq2G03ePxxOPvs1Cjx4osNgpIkNWFJM4PDgV/GGO9ZdDCEsCcwBXi+kIVJkpZNbe3XewTW1aVraGetIWbNSmFv2DCYMQM22QRGj06zgV26ZF2dJElFr8nWEiGEl2KMmzbx2FRg8xhjfWOPFxNbS0gqN1VVKQAurls3mDatraspgGnTUgAcOxY+/zzNBvbrB/vum6ZCJUnS1yxLa4mKEEKnGOOcxV6oMzCvFIKgJJWj6dObN14ynngCBg2C665Loe+ww1II3HbbrCuTJKkkLemfUMcD14cQqhoGcvevBa4obFmSpGVV2UQn2KbGi9qCBXDDDfDDH8IOO8Bdd6VTQadOhQkTDIKSJLVAk2EwxvhX4E7g4RDCByGED4CHgHtijH9pqwIlSc0zcCB07fr1sa5d03jJ+OILGDkSNt0UDjoo7QkcOhTefBPOPRfWXz/rCiVJKnlLbC0RY7wAuCCEsFLu+rM2qUqStMwaDokZMCAtDa2sTEGwJA6PeecdGDECLroIPv44zQb+3//BgQfCckttjStJkpohrz9ZDYGSVFpqakok/DV47rnUH/DKK2H+/BT++veHnXfOujJJktot/5lVkpSNGNMewMGD4Z570lrWk0+GPn1go42yrk6SpHbPMChJaltz5qRmiIMHw/PPwzrrwDnnpGaIq6+edXWSJJWNpTZkCiF0DSH8vxDCxbnrHiGE/QtfmiSVn9ra1CewoiLd1tZmXVEr+uAD+OtfU8PDE0+EDh1g3LjUN/B3vzMISpLUxvKZGbwMmAzslLueAfwDuLVQRUlSOaqtTZNj9bkurnV16RpKbP/f4l55BYYMScFv9mzYe++0H3CPPSCErKuTJKlsLXVmENgoxnguMA8gxjgb8E9vSWplAwZ8FQQb1Nen8ZITIzz8MPTsmdpDXHopHHkkTJkCd9wBe+5pEJQkKWP5zAzODSF0ASJACGEjYE5Bq5KkMjR9evPGi9K8eXDddWk/4KRJ8K1vpTR7xhmw9tpZVydJkhaRTxj8A6n5/AYhhFpgF+C4QhYlSeWosjItDW1svOjNmgVjx8KwYakx/MYbw6hRcMwx6ZRQSZJUdJa6TDTGeA9wECkAXgVUxxgfLGxZklQ6WuvQl4EDv5mbunZN40Wrrg769YMNNoBf/Qo23BBuvhlefBFOOcUgKElSEWtyZjCEsO1iQ+/kbitDCJUxxqcKV5YklYbWPPSl4fkDBqSloZWVKQgW5eExTz4JgwalJaEAhx6aQmF1dbZ1SZKkvIUYY+MPhPDAEr4vxhh3L0xJrau6ujpOmjQp6zIktVNVVY0v7ezWLXVMaFcWLIBbbkkh8F//gpVXTsm3d+8SWcsqSVJ5CiFMjjF+419sm5wZjDHuVtiSJKn0tYtDX5bmiy9SW4ghQ+C111LSHTw49QpceeWsq5MkScson6bznUMI/UII/wwhXB9C6BNC6NwWxUlSobTWPr+mJsTaxUTZO++kNauVlXD66akp/DXXpEDYt69BUJKkEpdPn8HxwObACOACYDPgikIWJUmF1LDPr64utcNr2Oe3LIGwJA99WZr//AeOPz6l5HPOgR/9CB55BCZOTHsDl8vnIGpJklTsmtwz+N8nhPBsjHGrpY0VK/cMSlpca+/zq60tkUNfliRGuPvutB/wnntSoj3+eDjrLOjRI+vqJElSCzR7z+Aing4h7BhjnJh7oR2AR1u7QElqK629z6+mpgTDX4M5c+DKK9MewClT4NvfTmn2lFPSslBJktRu5RMGdwCOCSE0/DWpEngxhPAf0qmi3ytYdZJUACXd3L21fPhhagp/wQXw3nuw5ZZw+eVw+OHQqVPW1UmSpDaQTxjcu+BVSFIbGjjw670BoR3s88vXq6+mU0Evvxxmz4a99oL+/WHPPSGErKuTJEltaKlhMMZYF0JYDdhg0efbdF5SqSqp5u6tIcbUF3DQILj5ZujYEY46Kp0IusUWWVcnSZIystQwGEL4C3Ac8DrQcNpMBEqi6bwkNaak9/nla/58uO66FAInTUp7AAcMSG0ivv3trKuTJEkZy2eZ6KHARjHGuYUuRpLUCj79FMaOhWHD0tRnjx5w4YVw7LHf7IMhSZLKVj5hcAqwKjCzsKVIklpk+vQUAC++GD77LPUHHD4cfvYzqMinrawkSSon+fzt4BxSe4m7Qgg3N3wVurCmhBD2DiG8HEJ4LYTwu6zqkNQ2amtTX8CKinS7LI3h271Jk+CII2DDDVMY3G8/ePJJeOgh6NnTIChJkhqVz8zgOODvwH+AhYUtZ8lCCB2AkcBPgBnAkyGEm2OML2RZl6TCqK39+qmfdXXpGspgv9/SLFwIt9yS9gM+8gistBL06QNnnllmPTIkSdKyyicMfhBjHF7wSvKzPfBajPENgBDC1UBPwDAotUMDBny9/QOk6wEDyjgM1tfDuHGpPcSrr6bgN2gQnHQSrLxy1tVJkqQSkk8YnBxCOAe4GZjTMJhRa4n1gDcXuZ4B7LD4k0IIvYBeAJX+C7lUsqZPb954u/buu6lB/KhR8NFH8P3vw9VXw8EHw3L5/K9ckiTp6/L5G8Q2udsdFxnLqrVEYx2R4zcGYhwDjAGorq7+xuOSSkNlZVoa2th42ZgyBQYPTmtm582Dn/88NYn/wQ9sEi9Jklokn6bzu7VFIXmaAWywyPX6wNsZ1SKpwAYO/PqeQUidEQYOzK6mNhEj3HNPWv55993QpUtaBtqnT2oTIUmS1AryWlsUQtgP2Bzo3DAWY/xzoYpagieBHiGE7sBbwOHAkRnUIakNNOwLHDAgLQ2trExBsN3uF5wzB666Ks0E/uc/qTH8wIFw8snwrW9lXZ0kSWpnlhoGQwgXAV2B3YCxwC+AJwpcV6NijPNDCGcAdwEdgEtjjM9nUYuktlFT047DX4MPP4SLLkp7At99F7bcEi67LLWL6NQp6+okSVI7lc/M4M4xxu+FEJ6LMf4phDAI+GehC2tKjPF24Pasfr4ktZpXX4WhQ1Pwmz0b9toLxo+HPfd0P6AkSSq4fMLg7NxtfQhhXeBDoHvhSpKkdixG+Ne/0n7Am2+Gjh3T1Ge/frDFFllXJ0mSykg+YfDWEMKqwHnAU6TTOy8uZFGS1O7Mnw/XX59C4JNPwuqrp82Qp5+e9gZKkiS1sXxOE/1L7u71IYRbgc4xxlmFLUuS2olPP4VLLoFhw1KfjB494MIL4dhj09GokiRJGWkyDIYQvg+8GWN8N3d9DHAwUBdC+GOM8aM2qlGSSs/06TB8OFx8cQqEP/pRut5/f6ioyLo6SZIklvQ3ktHAXIAQwo+AvwHjgVnkGrpLkhYzeTIceSRsuGE6HGbffdOy0IceSg3jDYKSJKlILGmZaIdFZv8OA8bEGK8nLRd9puCVSVKpWLgQbrst7Qd86CFYaaXUIP7MM1NzREmSpCK0xDAYQlguxjgf2APolef3SVJ5qK9PrSCGDIFXXknBb9AgOOkkWHnlrKuTJElaoiWFuquAh0IIH5DaSzwCEEL4DmmpqCSVp/feg5Ej00EwH34I1dVw1VXwi1/Acv5bmSRJKg1N/q0lxjgwhHAfsA5wd4wx5h6qAHq3RXGSVFSefx4GD4YJE2DevLQHsH9/+MEPbBIvSZJKzhL/CTvGOLGRsVcKV44kFZkY4d57Uwi8807o0gVOPDHtCdx446yrkyRJWmauZ5Kkxsydm5Z+Dh4Mzz0Ha68Nf/0rnHIKfOtbWVcnSZLUYoZBSVrURx/B6NEwYgS88w5ssQVcemlqF9GpU9bVSZIktRrDoCQBvPZa6gt42WXplNCf/hQuvxx+8hP3A0qSpHbJMCipfMUIjz2W2kHceGM6CbSmBvr1gy23zLo6SZKkgjIMSio/8+fDP/+Z9gP++9+w+urwP/8Dp58O66yTdXWSJEltwjAoqXx89hlccgkMGwbTpsF3vpP6BR57LKywQtbVSZIktSnDoKT27803YfhwGDMGPv0UfvjDtD9w//2hQ4esq5MkScqEYVBS+/XUU2k/4LXXpv2Bv/hFahL//e9nXZkkSVLmDIOS2peFC+G229J+wAcfhJVWgjPPTF/dumVdnSRJUtEwDEpqH2bPhvHjYcgQePll2GADOP98OOkkWGWVrKuTJEkqOoZBSaXtvffSITCjRsEHH0B1NVx1FRx8MHTsmHV1kiRJRcswKKk0vfBCWgo6YQLMnQs/+1naD/jDH9okXpIkKQ+GQUmlI0a4774UAu+4A7p0gRNOgD59YOONs65OkiSppBgGJRW/uXPh6qtTCHz2WVh7bfjLX+CUU2CNNbKuTpIkqSQZBiUVr48+gtGjYcQIeOcd2Hzz1DT+yCOhc+esq5MkSSpphkFJxef111NT+Esvhfp6+MlP4LLL4Kc/dT+gJElSKzEMSioOMcJjj6WloDfcAMstBzU10LcvfO97WVcnSZLU7hgGJWVr/vwU/gYNgn//G1ZbDX7/ezjjDFhnnayrkyRJarcMg5Ky8dlnaf/fsGEwbRpstBFccAEcdxyssELW1UmSJLV7hkFJbWvGDBg+HMaMgVmz4Ac/gCFDUp/ADh2yrk6SJKlsGAYltY2nn05LQa+5Ju0P/MUvoF8/2H77rCuTJEkqS4ZBSYWzcCHcfnsKgQ8+CCutBL17w5lnQlVV1tVJkiSVNcOgpNY3ezZccUVa/vnSS7D++nDeefDLX8Iqq2RdnSRJkjAMSmpNM2fCyJFw4YXwwQew3XZw5ZVpSWjHjllXJ0mSpEUYBiW13Isvpv6AV1wBc+akw2D694cf/cgm8ZIkSUXKMChp2cQI99+f9gPecQd07pzaQvTtC5tsknV1kiRJWgrDoKTmmTs3nQg6eDA88wystRb8+c9w6qmwxhpZVydJkqQ8GQYl5efjj2H0aBgxAt5+GzbbDMaOhZqaNCsoSZKkkmIYlLRkb7wBQ4fCpZfCF1/AnnvCJZfAXnu5H1CSJKmEGQYlNe6xx9J+wBtvhA4d4Mgj037ArbbKujJJkiS1AsOgpK/Mn5/C36BBMHEirLYa/Pa3cMYZsO66WVcnSZKkVmQYlASffZaWgQ4bBlOnwkYbwQUXpNNBV1gh6+okSZJUAIZBqZzNmJEOhBk9GmbNgl12SbOCP/95WhoqSZKkdsswKJWjp59Ooe+aa2DhQjj44NQkfocdsq5MkiRJbcQwKJWLhQtTc/hBg+CBB2DFFdNewDPPhO7ds65OkiRJbcwwKLV3s2fDhAmpSfxLL8H668O558Ivfwmrrpp1dZIkScqIYVBqr2bOhAsvTF/vvw/bbJNC4aGHQseOWVcnSZKkjBkGpfbmxRfTLOAVV8CcObD//mk/4I9/bJN4SZIk/ZdhUGoPYkz7AAcNgttvh86dU1uIPn1g002zrk6SJElFyDAolbK5c+Haa1MIfOYZWGst+NOf4NRTYc01s65OkiRJRcwwKJWijz+GMWNSj8C33oLNNoOxY6GmJs0KSpIkSUthGJRKydSpMHQoXHIJfPEF7LEHXHwx7LUXVFRkXZ0kSZJKiGFQKgWPP56Wgt5wA3ToAEccAX37wtZbZ12ZJEmSSpRhUCpWCxbAjTemEPj446kn4G9+kxrFr7de1tVJkiSpxBkGpWLz+edw6aVpOejUqbDhhmlv4HHHwYorZl2dJEmS2gnDoFQs3norhb7Ro+GTT2DnneH886Fnz7Q0VJIkSWpFhkEpa888k5aCXn01LFwIBx8M/frBjjtmXZkkSZLaMcOglIWFC+HOO1MIvP/+tPzz9NPhrLOge/esq5MkSVIZ8Cx6qS19+WVqBbHFFrDffvDyy3DuufDmm2mPYIkGwdpaqKpK3S2qqtK1JEmSipszg1JbmDkTRo2CkSPh/fdhm21gwgQ49FDo2DHr6lqkthZ69YL6+nRdV5euAWpqsqtLkiRJS+bMoFRIL72UklFlJfzxj7DDDmlZ6OTJKSmVeBAEGDDgqyDYoL4+jUuSJKl4OTMotbYY4cEH037A226Dzp3h2GNTk/hNN826ulY3fXrzxiVJklQcDINSa5k3D665BgYPhqefhjXXhD/9CU49Nd1vpyor09LQxsYlSZJUvFwmKrXUJ5+kQ2C6d4ejj/7qkJjp0+Hss9t1EAQYOBC6dv36WNeuaVySJEnFy5lBaVlNnZpOAL3kEvjiC9hjjxQC99orHatZJhoOiRkwIOXfysoUBD08RpIkqbgZBqXmmjgx7Qf85z9T6DviiNQkfuuts64sMzU1hj9JkqRSYxiU8rFgAdx4YwqBjz8Oq64Kv/419O4N662XdXWSJElSsxkGpSX5/HO47LK0HPSNN9K+wOHD4fjjYcUVs65OkiRJWmaGQakxb70FI0bA6NHpgJiddkqHxBxwAHTokHV1kiRJUosZBqVFPfNMag1x1VWwcCEcdFDaD7jTTllXJkmSJLUqw6C0cCHceWfaD3j//bDCCnDaaXDWWbDhhllXJ0mSJBWEYVDl68svYcKENBP44ovpIJi//x169UoHxEiSJEntmGFQ5ef99+HCC2HkyHR/663hiivg0ENh+eWzrk6SJElqE4ZBlY+XXoIhQ2D8+DQruO++0L8/7LYbhJB1dZIkSVKbMgyqfYsRHnww7Qe87Tbo1AmOOQb69oXvfjfr6iRJkqTMVGRdwOJCCH8MIbwVQngm97XvIo/9PoTwWgjh5RDCXlnWqSI3bx7U1sJ228Huu8O//w1/+ANMnw5jxhgEJUmSVPaKdWZwSIzx/EUHQgibAYcDmwPrAveGEDaOMS7IokAVqU8+SWFv+PDUK3DTTdP1UUdBly5ZVydJkiQVjWINg43pCVwdY5wDTA0hvAZsDzyebVkqClOnwrBhcMkl8PnnaR/g6NGwzz5QUXQT4JIkSVLmivVvyWeEEJ4LIVwaQlgtN7Ye8OYiz5mRG1M5mzgRDjkEvvOddDroAQfAU0+lfoH77WcQJK2WrapKvxVVVelakiRJymRmMIRwL/DtRh4aAIwC/gLE3O0g4ASgseMeYxOv3wvoBVBZWdkKFauoLFgAN92UDoV57DFYZRX41a+gd29Yf/2sqysqtbWpbWJ9fbquq0vXADU12dUlSZKk7IUYG81TRSGEUAXcGmPcIoTwe4AY4zm5x+4C/hhjXOIy0erq6jhp0qSC16o28PnncPnlMHQovP46dO8OffrACSfAiitmXFxxqqpKAXBx3brBtGltXY0kSZKyEEKYHGOsXny86NbQhRDWWeTyQGBK7v7NwOEhhE4hhO5AD+CJtq5PGXj7bfj976GyMs3+rbkm/OMf8OqrcOaZBsElmD69eeOSJEkqH8V4gMy5IYStSUtApwEnA8QYnw8hXAu8AMwHTvck0Xbu2Wdh8GC46qq0NPTAA6FfP9h556wrKxmVlY3PDLp6WpIkSUUXBmOMRy/hsYHAwDYsR20tRrjzzrQf8L77YIUV4JRT4KyzYKONsq6u5Awc+PU9gwBdu6ZxSZIklbeiC4MqU19+CRMmwJAh8MILsO66cM45cPLJsNpqS/9+NarhkJgBA9LS0MrKFAQ9PEaSJEmGQWXr/fdh1KjUFmLmTNhqKxg/Hg47DJZfPuvq2oWaGsOfJEmSvskwqGy8/HKaBRw3Ls0K7rtv2g+4++4QGusiIkmSJKk1GQbVdmKEhx5K+wFvvRU6dYKjj4a+fWGzzbKuTpIkSSorhkEV3rx5qRXEoEHw1FOwxhpw9tlw2mmw9tpZVydJkiSVJcOgCmfWLBgzBoYPhxkzYJNNYPToNBvYpUvW1UmSJEllzTCo1jdtGgwbBmPHwuefw667pkNi9t0XKiqyrk6SJEkShkG1pn//OzWJv+66FPoOOywdCrPttllXJkmSJGkxhkG1zIIFcPPNaT/go4/CKqtA//7QuzdssEHW1UmSJElqgmFQy+aLL+Cyy2DoUHj9dejWLbWKOPFEWGmlrKuTJEmStBSGQTXPO+/AiBFw0UXw8cewww5wzjlw4IGwnP85SZIkSaXCv70rP889l/YDXnklzJ+fwl+/frDzzjaJlyRJkkqQYVBNixHuuivtB7z3XujaFU4+Gc46C77znayrkyRJktQChkF905dfQm1tmgl84QVYZ520FLRXL1h99ayrkyRJktQKDIP6ygcfpH6AF1wAM2fC974H48bB4YfD8stnXZ0kSZKkVmQYFLzySjoJdNw4mD0b9tkn7QfcYw/3A0qSJEntlGGwXMUIDz+c9gPeeit07AhHHw19+8Lmm2ddnSRJkqQCMwyWm3nz4LrrUgicPBnWWAP+3/+D006DtdfOujpJkiRJbcQwWC5mzYKLL4bhw+HNN2HjjVOvwGOOgS5dsq5OkiRJUhszDLZ3dXUwbBiMHQuffQY//jGMHAn77QcVFVlXJ0mSJCkjhsH26okn0lLQ669P14cdlg6F2W67bOuSJEmSVBQMg+3JggVwyy0pBP7rX7DyyulAmDPPhA02yLo6SZIkSUXEMNgefPEFXH45DB0Kr70G3bqlVhEnnJACoSRJkiQtxjBYyt55JzWIv+gi+Ogj2H57uOYaOOggWM63VpIkSVLTTAyl6LnnYPBguPJKmD8fDjgg7QfcZRebxEuSJEnKi2GwVMQId9+d9gPecw907Qq9ekGfPvCd72RdnSRJkqQSYxgsdnPmQG1tmgl8/nlYZx34v/+Dk0+G1VfPujpJkiRJJcowWKw+/BBGjUp7At97D7bcMh0Sc/jh0KlT1tVJkiRJKnGGwWLzyivpJNBx42D2bNh7b+jfH/bYw/2AkiRJklqNYbAYxAiPPJL2A95yC3TsCEcdlQ6F2XzzrKuTJEmS1A4ZBrM0bx5cd13aDzhpEnzrW/C//wunnQbf/nbW1UmSJElqxwyDWZg1C8aOhWHD4M03YeON0/7AY45Jp4RKkiRJUoEZBttSXV0KgGPHwmefwY9/DCNHwn77QUVF1tVJkiRJKiOGwbbw5JNpP+B116XrQw9N+wGrq7OtS5IkSVLZMgwWyoIFcOutKQQ+8gisvDL07Qu9e0NlZdbVSZIkSSpzhsHWVl+f+gEOGQKvvZaC3+DBcOKJKRBKkiRJUhEwDLaWd99NDeJHjYKPPoLvfx+uvhoOPhiW87dZkiRJUnExpbTUf/6TZv6uvDK1iujZMzWJ32UXm8RLkiRJKlqGwWURI9xzT9oPePfd0KULnHQS9OkDPXpkXZ0kSZIkLZVhsDnmzEkzgIMHw5QpqTH8wIFw8smpYbwkSZIklQjDYD4+/BAuuijtCXz3XdhyS7jsMjjiCOjUKevqJEmSJKnZ7HS+JK++CqedBhtsAP/7v7DVVmlZ6LPPwnHHGQTVLLW1UFUFFRXptrY264okSZJUzpwZbMqnn6bwt2AB1NSkJvFbbJF1VSpRtbXQq1fqPAJQV5euIf3nJUmSJLW1EGPMuoaCqq6ujpMmTVq2b77hBthpp7Q3UGqBqqoUABfXrRtMm9bW1UiSJKmchBAmxxirFx93ZnBJDjww6wrUTkyf3rxxSZIkqdDcM6h2rVj26VVWNm9ckiRJKjTDoNqthn16dXWpNWTDPr0sAuHAgdC169fHunZN45IkSVIWDINqtwYM+OrAlgb19Wm8rdXUwJgxaY9gCOl2zBgPj5EkSVJ2PEBG7VZFRZoRXFwIsHBh29cjSZIkZaGpA2ScGVS75T49SZIkqWmGQbVb7tOTJEmSmmYYVLvlPj1JkiSpafYZVLtWU2P4kyRJkhrjzKAkSZIklSHDoCRJkiSVIcOgJEmSJJUhw6AkSZIklSHDoCRJkiSVIcOgJEmSJJUhw6AkSZIklSHDoCRJkiSVIcOgJEmSJJUhw6AkSZIklSHDoCRJkiSVIcOgJEmSJJUhw6AkSZIklSHDoCRJkiSVIcOgJEmSJJUhw6DyVlsLVVVQUZFua2uzrkiSJEnSslou6wJUGmproVcvqK9P13V16Rqgpia7uiRJkiQtG2cGlZcBA74Kgg3q69O4JEmSpNJjGFRepk9v3rgkSZKk4mYYVF4qK5s3LkmSJKm4GQaVl4EDoWvXr4917ZrGJUmSJJUew2ATPDnz62pqYMwY6NYNQki3Y8Z4eIwkSZJUqjIJgyGEQ0IIz4cQFoYQqhd77PchhNdCCC+HEPZaZHy7EMJ/co8NDyGEQtXXcHJmXR3E+NXJmQZCmDYNFi5MtwZBSZIkqXRlNTM4BTgIeHjRwRDCZsDhwObA3sCFIYQOuYdHAb2AHrmvvQtVnCdnSpIkSWrvMgmDMcYXY4wvN/JQT+DqGOOcGONU4DVg+xDCOsDKMcbHY4wRGA8cUKj6PDlTkiRJUntXbHsG1wPeXOR6Rm5svdz9xccLwpMzJUmSJLV3BQuDIYR7QwhTGvnquaRva2QsLmG8qZ/dK4QwKYQw6f33329u6Z6cKUmSJKndW65QLxxj3HMZvm0GsMEi1+sDb+fG129kvKmfPQYYA1BdXd1kaGxKw8EoAwakpaGVlSkIemCKJEmSpPai2JaJ3gwcHkLoFELoTjoo5okY4zvAZyGEHXOniB4D3FTIQjw5U5IkSVJ7llVriQNDCDOAnYDbQgh3AcQYnweuBV4A7gROjzEuyH3bqcBY0qEyrwN3tHnhkiRJktROhHQ4Z/tVXV0dJ02alHUZkiRJkpSJEMLkGGP14uPFtkxUkiRJktQGDIOSJEmSVIYMg5IkSZJUhgyDkiRJklSGDIOSJEmSVIYMg5IkSZJUhgyDkiRJklSGDIOSJEmSVIYMg5IkSZJUhgyDkiRJklSGDIOSJEmSVIYMg5IkSZJUhgyDkiRJklSGQowx6xoKKoTwPlCXdR0CYA3gg6yL0FL5PpUO36vS4PtUOnyvSoPvU+nwvSoe3WKMay4+2O7DoIpHCGFSjLE66zq0ZL5PpcP3qjT4PpUO36vS4PtUOnyvip/LRCVJkiSpDBkGJUmSJKkMGQbVlsZkXYDy4vtUOnyvSoPvU+nwvSoNvk+lw/eqyLlnUJIkSZLKkDODkiRJklSGDIMqmBDCISGE50MIC0MITZ4kFUKYFkL4TwjhmRDCpLasUc16n/YOIbwcQngthPC7tqxRSQhh9RDCPSGEV3O3qzXxPD9TGVjaZyQkw3OPPxdC2DaLOstdHu/TriGEWbnPzzMhhLOzqLPchRAuDSHMDCFMaeJxP09FIo/3ys9UETMMqpCmAAcBD+fx3N1ijFt7/HAmlvo+hRA6ACOBfYDNgCNCCJu1TXlaxO+A+2KMPYD7ctdN8TPVhvL8jOwD9Mh99QJGtWmRas7/yx7JfX62jjH+uU2LVIPLgb2X8Lifp+JxOUt+r8DPVNEyDKpgYowvxhhfzroOLVme79P2wGsxxjdijHOBq4Geha9Oi+kJjMvdHwcckF0pWkw+n5GewPiYTARWDSGs09aFljn/X1YiYowPAx8t4Sl+nopEHu+ViphhUMUgAneHECaHEHplXYwatR7w5iLXM3JjaltrxxjfAcjdrtXE8/xMtb18PiN+jrKX73uwUwjh2RDCHSGEzdumNDWTn6fS4meqSC2XdQEqbSGEe4FvN/LQgBjjTXm+zC4xxrdDCGsB94QQXsr9K5NaSSu8T6GRMY8iLoAlvVfNeBk/U20vn8+In6Ps5fMePAV0izF+HkLYF7iRtBRRxcXPU+nwM1XEDINqkRjjnq3wGm/nbmeGEG4gLePxL66tqBXepxnABotcrw+83cLXVCOW9F6FEN4LIawTY3wntxxqZhOv4Weq7eXzGfFzlL2lvgcxxk8XuX97COHCEMIaMcYP2qhG5cfPU4nwM1XcXCaqTIUQVgghrNRwH/gp6UATFZcngR4hhO4hhOWBw4GbM66pHN0MHJu7fyzwjVldP1OZyeczcjNwTO4UxB2BWQ3LftVmlvo+hRC+HUIIufvbk/6u9GGbV6ql8fNUIvxMFTdnBlUwIYQDgRHAmsBtIYRnYox7hRDWBcbGGPcF1gZuyP0/YjngyhjjnZkVXYbyeZ9ijPNDCGcAdwEdgEtjjM9nWHa5+htwbQjhRGA6cAiAn6nsNfUZCSGcknv8IuB2YF/gNaAeOD6restVnu/TL4BTQwjzgdnA4TFGlx+2sRDCVcCuwBohhBnAH4CO4Oep2OTxXvmZKmLB90KSJEmSyo/LRCVJkiSpDBkGJUmSJKkMGQYlSZIkqQwZBiVJkiSpDBkGJUmSJKkMGQYlSUUjhPCtEMIzua93Qwhv5e5/EkJ4oY1rOSCEsNki138OIey5DK9TFUJotNdjCGHzEML9IYRXQgivhxD+FEJo9T+bl/RrCSE8GEKobu2fKUkqfoZBSVLRiDF+GGPcOsa4NXARMCR3f2tgYWv/vBDCkvrtHgD8N0DFGM+OMd7bij+7C6lx9t9ijBsDWwLbA2e11s9YxAEU8NciSSpNhkFJUqnoEEK4OITwfAjh7lyYIoSwUQjhzhDC5BDCIyGETXPj3UII94UQnsvdVubGLw8hDA4hPAD8vbHvDyHsDPwcOC83M7lR7vt+kXuN74cQHgshPBtCeCKEsFJuBvCREMJTua+dl/LrORJ4NMZ4N0CMsR44A/h17mf8MYTwq4YnhxCmhBCqcvdvzNX7fAih1yLP+TyEMDBX18QQwtpL+7UsKoTw0xDC47n6/xFCWDE3/rcQwgu538vzm//WSZKKkWFQklQqegAjY4ybA58AB+fGxwC9Y4zbAb8CLsyNXwCMjzF+D6gFhi/yWhsDe8YY+zf2/THGx0izdr/OzVS+3vCNIYTlgWuAs2KMWwF7ArOBmcBPYozbAoct9vMaszkwedGB3M/pEkJYdSnfe0Ku3mrgzBDCt3LjKwATc3U9DPxySb+WRYUQ1gD+N/f7si0wCegXQlgdOBDYPPd7+del1CZJKhFLWh4jSVIxmRpjfCZ3fzJQlZu52hn4Rwih4Xmdcrc7AQfl7l8BnLvIa/0jxrhgKd/flE2Ad2KMTwLEGD8FCCGsAFwQQtgaWEAKnEsSgNjE+NKcGUI4MHd/A1JQ/hCYC9yaG58M/CSP12qwI2kp6aO534vlgceBT4EvgbEhhNsWeX1JUokzDEqSSsWcRe4vALqQVrh8kttXuDSLBq8vcrfN+f4GTYW4vsB7wFa51/1yKa/zPPCjr71wCBsCH8QYPwkhzOfrK3g6556zK2k2cqcYY30I4cGGx4B5McaG2hbQvD/nA3BPjPGIbzwQwvbAHsDhpKWsuzfjdSVJRcplopKkkpWblZsaQjgEICRb5R5+jBReAGqAfzXz+z8DVmrkx74ErBtC+H7ue1bKHUSzCmnGcCFwNNBhKeXXAj9Y5FTPLqSlpX/IPT4N2Db32LZA99z4KsDHuSC4KWlGb2ma+rUsaiKwSwjhO7mf2TWEsHFu9nSVGOPtQB/SYT6SpHbAMChJKnU1wIkhhGdJs209c+NnAseHEJ4jhbOmTuls6vuvBn4dQng6hLBRw5NjjHNJewJH5L7nHtLM3IXAsSGEiaQlol+wBDHG2aSDXQaEEF4BPiAdKFObe8r1wOohhGeAU4FXcuN3Asvlfl1/IYW4pWn017JYPe8DxwFX5V57IrApKUTemht7iDQDKklqB8JXq0kkSVJWQggHAIOB3WKMdRmXI0kqA4ZBSZIkSSpDLhOVJEmSpDJkGJQkSZKkMmQYlCRJkqQyZBiUJEmSpDJkGJQkSZKkMmQYlCRJkqQyZBiUJEmSpDL0/wEUEyniz0sQLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.gofplots        import  qqplot\n",
    "qqplot(residuals,line=\"s\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "### residual points are falling approximately along this reference line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forecast Using Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vivekanand\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    }
   ],
   "source": [
    "model_TES = ExponentialSmoothing(df,trend='additive',seasonal='additive',initialization_method='estimated')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ExponentialSmoothing Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Avg spending</td>     <th>  No. Observations:  </th>        <td>605</td>      \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ExponentialSmoothing</td> <th>  SSE                </th>    <td>152977.356</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Optimized:</th>                <td>False</td>        <th>  AIC                </th>     <td>3369.354</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Trend:</th>                  <td>Additive</td>       <th>  BIC                </th>     <td>3417.812</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seasonal:</th>               <td>Additive</td>       <th>  AICC               </th>     <td>3369.970</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seasonal Periods:</th>           <td>7</td>          <th>  Date:              </th> <td>Sat, 03 Sep 2022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Box-Cox:</th>                  <td>False</td>        <th>  Time:              </th>     <td>13:56:24</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Box-Cox Coeff.:</th>           <td>None</td>         <th>                     </th>         <td> </td>       \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>                  <th>coeff</th>                <th>code</th>               <th>optimized</th>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smoothing_level</th>    <td>           0.9542595</td> <td>               alpha</td> <td>               False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smoothing_trend</th>    <td>           0.0060734</td> <td>                beta</td> <td>               False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smoothing_seasonal</th> <td>           0.0015891</td> <td>               gamma</td> <td>               False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_level</th>      <td>           62.945810</td> <td>                 l.0</td> <td>               False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_trend</th>      <td>          -0.2989264</td> <td>                 b.0</td> <td>               False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.0</th>  <td>          -0.6104694</td> <td>                 s.0</td> <td>               False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.1</th>  <td>          -0.9283265</td> <td>                 s.1</td> <td>               False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.2</th>  <td>           0.7441735</td> <td>                 s.2</td> <td>               False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.3</th>  <td>           1.3285306</td> <td>                 s.3</td> <td>               False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.4</th>  <td>          -0.1833265</td> <td>                 s.4</td> <td>               False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.5</th>  <td>          -0.2179694</td> <td>                 s.5</td> <td>               False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.6</th>  <td>          -0.1326122</td> <td>                 s.6</td> <td>               False</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                       ExponentialSmoothing Model Results                       \n",
       "================================================================================\n",
       "Dep. Variable:             Avg spending   No. Observations:                  605\n",
       "Model:             ExponentialSmoothing   SSE                         152977.356\n",
       "Optimized:                        False   AIC                           3369.354\n",
       "Trend:                         Additive   BIC                           3417.812\n",
       "Seasonal:                      Additive   AICC                          3369.970\n",
       "Seasonal Periods:                     7   Date:                 Sat, 03 Sep 2022\n",
       "Box-Cox:                          False   Time:                         13:56:24\n",
       "Box-Cox Coeff.:                    None                                         \n",
       "=================================================================================\n",
       "                          coeff                 code              optimized      \n",
       "---------------------------------------------------------------------------------\n",
       "smoothing_level               0.9542595                alpha                False\n",
       "smoothing_trend               0.0060734                 beta                False\n",
       "smoothing_seasonal            0.0015891                gamma                False\n",
       "initial_level                 62.945810                  l.0                False\n",
       "initial_trend                -0.2989264                  b.0                False\n",
       "initial_seasons.0            -0.6104694                  s.0                False\n",
       "initial_seasons.1            -0.9283265                  s.1                False\n",
       "initial_seasons.2             0.7441735                  s.2                False\n",
       "initial_seasons.3             1.3285306                  s.3                False\n",
       "initial_seasons.4            -0.1833265                  s.4                False\n",
       "initial_seasons.5            -0.2179694                  s.5                False\n",
       "initial_seasons.6            -0.1326122                  s.6                False\n",
       "---------------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_TES = model_TES.fit(smoothing_level=0.9542595,smoothing_trend=0.00607340,smoothing_seasonal=0.00158913,optimized=False)\n",
    "model_TES.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast= model_TES.forecast(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df,label='Data')\n",
    "plt.plot(forecast,label='forecast - TES')\n",
    "plt.legend(loc='best')\n",
    "plt.grid();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### END"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "ML_1_40_MARKS_SET_2_EXAM_Paper.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}