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Time series forecasting using LSTM with multiple time series of the same type

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Hi all,
I'm following this Time Series Forecasting example using LSTM networks.
I'm wondering if it is possible to train the network for more than one similar time histories.
I mean that if I have a lot of similar curves could I train the network using the information from all of them?
Using one single trend to train a model seem to be pretty restrictive.

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Dinesh Yadav
Dinesh Yadav on 27 Mar 2020
Yes you can retrain the already trained network on new data (provided your data and prediction are of same data type, the length of sequences must be same while giving input to network). You can retrain the network parameters on multiple time series data. However depending on application it may or may not give you good results. For example if correlation between two time series data is high you will get a prediction encompassing properties of both time series it may be better, however if there is no correlation between two time series data your results will suffer.

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Dinesh Yadav
Dinesh Yadav on 27 Mar 2020
Yes when training with multiple series you will loose information from first net because it will try to best fit the two/three (how many you give) series you are trying to fit. In crude form you can say it will be some form of avergae of those series. Thats why its very important the multiple time series have high correlation therefore your information loss will be minimum. There is no point in training multiple series with no correlation, that model will not fit anything properly. Yes your workflow is correct.
Zhimin Xi
Zhimin Xi on 2 Oct 2020
retrain individually is not a good idea. I'm wondering why Matlab cannot implement such a simple extension to train multiple time series data directly. The difference is when calculating the MSE, you'll consider all time series data instead of only one time series data. I think this limitation is pretty bad for Matlab neural network toolbox.

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