CODEWITHSUNDEEP
pythonmachine-learningkerastime-series
Maharshi
Maharshi

Reputation: 36

Multi Step Time Series Forecasting with Multiple Features

So I'm at very beginner level of Machine Learning and I want to forecast multiple samples of time series. The time series contains samples at every 15 minutes and I have to forecast samples for next 3 days. So approximately 288 samples in future.

My time series have other categorical features also so I implemented one model based on this answer.

I read about encoder-decoder for seq2seq time series forecasting. But couldn't understand much regarding how to implement it and combine it with multiple categorical features.

  1. Am I going in the right direction by following that answer?
  2. Will LSTM work properly even for a large dimension of Y (in my case 288 time steps into future).
  3. I'm considering last 7 days samples as X so my input shape for lstm is (no of samples, 672, 1). Is that okay?
  4. Should I go for encoder-decoder? If yes then can anyone please provide me some more insight and maybe a good tutorial.

Thanks in advance.

Upvotes: 1

Views: 2274

Answers (1)

KonstantinosKokos
KonstantinosKokos

Reputation: 3453

  1. Yes.
  2. Depends on how much data you have and how learnable your problem is.
  3. The more data you use the better.
  4. An encoder-decoder architecture is just a fancy name for 'feedforward your hidden LSTM states'. I don't see a reason why you would need to use it in your case.

Upvotes: 2

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