How can I use LSTM networks to solve a time series regression problem?

How can I use LSTM networks to solve a time series regression problem? I would like to train a LSTM regression network using all but the last 10 time steps and then test it to see how well the model has done using the last 10 steps of time series that were not used in the training.

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To forecast the values of future time steps of a sequence, you can train a sequence-to-sequence regression LSTM network, where the responses are the training sequences with values shifted by one time step. That is, at each time step of the input sequence, the LSTM network learns to predict the value of the next time step.

Please refer to the attached example, “TimeSeriesForecastLSTM.mlx”, which demonstrates how to forecast time-series data using a long short-term memory (LSTM) network.

This example trains an LSTM network to forecast the number of chickenpox cases given the number of cases in previous months. The training data contains a single time series, with time steps corresponding to months and values corresponding to the number of cases.

Further, you mentioned that you need to forecast the values for the last 10 steps. To forecast the values of multiple time steps in the future, you can use the “predictAndUpdateState” function to predict time steps one at a time and update the network state at each prediction. Please refer to the documentation of the “predictAndUpdateState” function for more

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