Standardize the test data using the same parameters as the training data. Standardize Data For a better fit and to prevent the training from diverging, standardize the training data to have zero mean and unit variance. NumTimeStepsTrain = floor(0.9*numel(data)) XTrain = data(1:numTimeStepsTrain) YTrain = data(2:numTimeStepsTrain+1) XTest = data(numTimeStepsTrain+1:end-1) YTest = data(numTimeStepsTrain+2:end) That is, at each time step of the input sequence, the LSTM network learns to predict the value of the next time step. To forecast the values of future time steps of a sequence, specify the responses to be the training sequences with values shifted by one time step. Train on the first 90% of the sequence and test on the last 10%. data = chickenpox_dataset data = figure plot(data) xlabel('Month') ylabel('Cases') title('Monthy Cases of Chickenpox') The output is a cell array, where each element is a single time step. chickenpox_dataset contains a single time series, with time steps corresponding to months and values corresponding to the number of cases. Load Sequence Data Load the example data. The example trains an LSTM network to forecast the number of chickenpox cases given the number of cases in previous months. This example uses the data set chickenpox_dataset. To forecast the values of multiple time steps in the future, use the predictAndUpdateState function to predict time steps one at a time and update the network state at each prediction. 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.

Time Series Forecasting Using Deep Learning This example shows how to forecast time series data using a long shortterm memory (LSTM) network. Time Series Forecasting Using Deep Learning - MATLAB & Simulink

Matlab time series prediction