CODEWITHSUNDEEP
tensorflowtime-serieslstm
Debashis Sahoo
Debashis Sahoo

Reputation: 5912

HOW to train LSTM for Multiple time series data - both for Univariate and Multivariate scenario?

I have data for hundreds of devices(pardon me, I am not specifying much detail about device and data recorded for devices). For each device, data is recorded per hour basis.

Data recorded are of 25 dimensions.

I have few prediction tasks

time series forecasting

where I am using LSTM. As because I have hundreds of devices, and each device is a time series(multivariate data), so all total my data is a Multiple time series with multivariate data.

To deal with multiple time series - my first approach is to concatenate data one after another and treat them as one time series (it can be both uni variate or multi variate) and apply LSTM and train my LSTM model.

But by this above approach(by concatenating time series data), actually I am loosing my time property of my data, so I need a better approach.

Please suggest some ideas, or blog posts.

Kindly don't confuse with Multiple time series with Multi variate time series data.

Upvotes: 3

Views: 5389

Answers (1)

Simon Müller
Simon Müller

Reputation: 368

You may consider a One-fits-all model or Seq2Seq as e.g. this Google paper suggests. The approach works as follows:

  • Let us assume that you wanna make a 1-day ahead forecast (24 values) and you are using last 7 days (7 * 24 = 168 values) as input.

  • In time series analysis data is time dependent, such that you need a validation strategy that considers this time dependence, e.g. by rolling forecast approach. Separate hold-out data for testing your final trained model.

  • In the first step you will generate out of your many time series 168 + 24 slices (see the Google paper for an image). The x input will have length 168 and the y input 24. Use all of your generated slices for training the LSTM/GRU network and finally do prediction on your hold-out set.

Good papers on this issue:

Kaggle Winning Solution

List is not comprehensive, but you can use it as a starting point.

Upvotes: 2

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