使用具有Keras的LSTM进行时间序列预测的不同大小的预测因子

Question

I would like to predict time series values X using another time series Y and the past value of X .In detail, I would like to predict X at time t ( Xt ) using ( Xt-p ,..., Xt-1 ) and ( Yt-p ,..., Yt-1 , Yt ) with p the dimension of the "look back". So, my problem is that I do not have the same length for my 2 predictors. Let's use a exemple to be clearer.

If I use a timestep of 2, I would have for one observation : [(Xt-p,Yt-p),...,(Xt-1,Yt-1),(??,Yt)] as input and Xt as output. I do not know what to use instead of the ??

I understand that mathematically speaking I need to have the same length for my predictors, so I am looking for a value to replace the missing value.

I really do not know if there is a good solution here and if I could to something so any help would be greatly appreciated.

Cheers !

PS : you could see my problem as if I wanted to predict the number of ice cream sell one day in advance in a city using the forcast of weather for the next day. X would be the number of ice cream and Y could be the temperature.

Answer 1

You could eg do the following:

input_x = Input(shape=input_shape_x)
input_y = Input(shape=input_shape_y)
lstm_for_x = LSTM(50, return_sequences=False)(input_x)
lstm_for_y = LSTM(50, return_sequences=False)(input_y)
merged = merge([lstm_for_x, lstm_for_y], mode="concat") # for keras < 2.0
merged = Concatenate([lstm_for_x, lstm_for_y])
output = Dense(1)(merged)

model = Model([x_input, y_input], output)
model.compile(..)
model.fit([X, Y], X_next)

Where X is an array of sequences, X_forward is X p -steps ahead and Y is an array of sequences of Ys .