I have a problem while developing an LStm model. I have 4 feaures that I want to use to make a prediction. When I test my model with a single feaure I get average results but when I test with all 4 feaures the results are even worse. I don't understand where the problem could come from. When we add feaures to the LSTM model the performances should be better. This means that the model is more wrong when it has more variables. I want to clarify that when I test the feaures independently I always have the same performance. My model is really basic.
model = Sequential() model.add(LSTM(nb_units, input_shape = my_shape )) Dense(1,activation='sigmoid') model.compile(loss='mean_absolute_error',optimizer='adam') history = model.fit(X_train, y_train, epochs=ep, validation_data=(X_val, y_val), verbose=2, shuffle=False)
- MAE feature 1 : 4
- MAE feature 2 : 4
- MAE feature 3 : 4
- MAE feature 4 : 4
- MAE feature 1,2,3,4 : 7
Do you have any idea where the problem could come from?
So my input data with the feature "pressure" will be ( with the use of a mask):
[ [,], ,], ,], [,], ,], ,]] ]