title: Can you train LSTM on a dataset with several time-series?
body: <p>I want to use LSTM in the problem of sports prediction.</p>
  <p>I know you can use LSTM to predict time-series values such as financial data ...</p>
  <p>In such time-series each value is part of the same sequence.</p>
  <p>In my idea I have several relatively short sequences(around 50), I don't think it makes sense to train a separate model for each sequence.</p>
  <p>Can I use LSTM to train it on the whole dataset, but only use the attributes of the specific sequence as the features. To illustrate, I use last N games of each team to predict the result of the current one.</p>
  <p>Based on this <a href="https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/" rel="nofollow noreferrer">tutorial</a> it seems that you have to construct the dataset so that you include previous examples next to the current, which means you can define a custom order.</p>
  <p>My question is whether the LSTM only takes the previous examples defined in the dataset as the basis of the prediction or does it have a memory of previous examples? If the latter is through my approach wouldn't make sense.</p>