<p>The real potential of LSTM models for time series forecasting can be exploiting by building a global model using all the time series, instead as a univariate model, which actually ignores any cross series information available across your time series.</p>

<p>We implement the use case you are referring to by introducing a '<strong>Moving Window Approach</strong>' strategy that involves modeling a multiple input and output mapping, where you can pool time series that have different lengths. More detailed discussion of this strategy is described in <strong>section 3.4</strong> on our paper[1]. Here, you basically produce multiple input and output tuples for the given set of time series you have and then pool them to together for the LSTM training purposes. This accommodates even if you have time series with different lengths.</p>

<p>[1] <a href="https://arxiv.org/pdf/1710.03222.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1710.03222.pdf</a></p>
LSTM - time series predictions
<p>I am following this tutorial <a href="https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/" rel="nofollow noreferrer">LSTM</a> and I wonder how to map this to a multi-time series input. I have a dataset of several time-series and I want to predict for each time series the future. I don't know how to scale LSTM to several time-series.</p>

<p>The aim is to avoid to make a model for each time series as I have 40k of time series.</p>

<p>Thank you</p>