title: Can anyone explain the sliding windows are used in LSTMs?
body: <p>As far as I know, the LSTM has a loop that inputs from the last hidden layer resulting the model to have a long short term memory. Then why use sliding windows?</p>
  <p>Can you also explain how sliding window works in 'programming with keras' perspective?(or leave a link that explains the working) For example:</p>
  <pre><code>slide_window = 10
  train1, train2 = slwindow_df(train, slide_window)
  test1, test2 = slwindow_df(test, slide_window)
  
  model.compile(loss ='mean_squared_error', optimizer = 'adam')
  model.fit(train1, train2, epochs =60, batch_size=1, verbose=2)
  </code></pre>
  <p>Also, I guess this is a really dumb question but is it ok to use different sliding windows while creating a model? (With different lengths)</p>