I am trying to create a neural network with keras that will have as input N multivariate time-series and as target output N time-series. I converted the time-series to a supervised problem with the window or lag method. As input I have a 4D matrix (samples, variables, sequence, lag) and as output a 2D matrix (samples, sequence). I found similar examples that used CNN+LSTM models, but I have difficulties applying them. In case it helps, I have train_X, train_y, test_X, test_y with dimensions (112, 5, 7998, 2) (112, 7998) (29, 5, 7998, 2) (29, 7998)
I have tried applying and removing the TimeDistributed Keras wrapper to the CNN part only and the whole model. The relevant part of the code is bellow.
model = Sequential()
model.add(TimeDistributed(Conv2D(filters=32, kernel_size=(1, 80), activation='relu', padding='same', input_shape=(train_X.shape[1], train_X.shape[2], train_X.shape[3]))))
model.add(TimeDistributed(MaxPool2D(pool_size=(1, 2),strides=1)))
model.add(TimeDistributed(Dropout(0.5)))
model.add(TimeDistributed(Flatten()))
model.add(TimeDistributed(LSTM(100, return_sequences=True)))
model.add(TimeDistributed(Dropout(0.2)))
model.add(TimeDistributed(Dense(units=1)))
model.compile(loss='mean_squared_error', optimizer='adam')
I get an index error.
IndexError: list index out of range