Update README.md

README.md

@@ -24,13 +24,18 @@ This project involves tracking extreme and prolonged warming events in sea surfa
 | TBATS | Exponential smothing state space model with Box-Cox Transformation |  ? |
 | AR | AutoRegressive order(6) |  MSE = 0.10 |
 | ARMA | AutoRegressive Moving Average, order(6,2) | MSE = 0.13 |
-| SARIMAX | Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors, order(6,2,1)(6,2,1,4) | MSE = 0.13 | 
+| SARIMAX | Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors, order(6,2,1)(6,2,1,4) | MSE = 0.13 |
 | Persistence | "walk-forward" validation | MSE = 0.28 (1-day), 0.29 (3-day), 0.65 (9-day) |
 |LSTM_1var | 5 neuron LSTM, 1 neuron Dense output layer, tanh activation, SGD optimizer, fit with 20 epochs, 520 batch size, forecasting 5-day sequence based on the previous 20 days, features are only SST | |
 | LSTM_adjacent | LSTM trained on the SST from a neighboring grid cells | *in prog.*|
 | LSTM_5var | 30 neuron LSTM, 1 neurton Dense output, tanh activation, SGD optimizer, 20 epochs, 800 batch_size, forecasting 21-day sequences based on the previous 100 days, features include SST, AirT, RH, WS, and SLP | |   
 | LSTM_2var_PCA | features include SST and the first 2 PCs of SST, AirT, RH, WS and SLP | |
-| XGB_1var_binary | Gradient Boosted Machines for event classification using SST alone to predict marine heatwave class, labels are binary (0=no event, 1=MHW), forecasts 30 days using the past 40, weights are given to circumvent class imbalance | |
+| XGB_1var_binary | Gradient Boosted Machines for event classification using SST alone to predict marine heatwave class, labels are binary (0=no event, 1=MHW), forecasts 30 days using the past 40, weights are given to circumvent class imbalance | 
+| XGB_5var_binary | Gradient Boosted Machines for event classification using SST, AirT, RH, WS, and SLP to predict marine heatwave class, **labels are binary** (0=no event, 1=MHW), forecasts 30 days using the past 40, weights are given to circumvent class imbalance |  |
+| XGB_1var_categ | Gradient Boosted Machines for event classification using SST alone to predict marine heatwave class, **labels are categorical** for MHWs only (0=no event, 1=moderate, 2=strong, 3=severe, 4=extreme), forecasts 30 days using the past 40 | |
+| XGB_5var_categ | Gradient Boosted Machines for event classification using SST, AirT, RH, WS, and SLP to predict marine heatwave class, **labels are categorical** for MHWs only (0=no event, 1=moderate, 2=strong, 3=severe, 4=extreme), forecasts 30 days using the past 40 |  |
+| XGB_1var_categ_mhw_mcw | Gradient Boosted Machines for event classification using SST alone to predict **both marine heatwave and coldwave** class, labels are categorical, positive for MHWs & negative for MCWs  (0=no event, 1=moderate, 2=strong, 3=severe, 4=extreme), forecasts 30 days using the past 40 | |
+| XGB_5var_categ_mhw_mcw | Gradient Boosted Machines for event classification using SST, AirT, RH, WS, and SLP to predict **both marine heatwave and coldwave** class, labels are categorical, positive for MHWs & negative for MCWs (0=no event, 1=moderate, 2=strong, 3=severe, 4=extreme), forecasts 30 days using the past 40 |  |
 
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