My question is somehow similar to this question, but not satisfied with the answer. I have 100 samples, each sample consists of 6 time-series, (let's say [X1, X2, X3, X4, X5, Y]) of length 200. Each sample is independent of each other. I want to build a time-series model, which will take one sample (300, 5) as input and predict Y (300, 1) as output.

LSTM relates the various windows (in my case sample) while making the model, which is undesirable in my case. Though I tried I tried it and but could not achieve good results. This is the actual and predicted value of some of the samples. We observe that, prediction is happening in a narrow band between 35000 to 50000 for most of the cases. My intuition is LSTM has learnt only the latest sample passed and making prediction based on that. enter image description here Further more, I thought of using ARIMAX, but same problem will arise there as well. I tried CNN as well which was predicting better than LSTM but not close to actual values.

So my question is: what will be best strategy to train such model where it takes a 2D time series data as input, learn the temporal dependency of the X and Y and predict time series sequence of Y.



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