Multi step ahead forecasting with LSTM

numTimeStepsTrain = floor(0.9*numel(data));% 90% for training 10%for testing
dataTrain = data(1:numTimeStepsTrain+1);
dataTest = data(numTimeStepsTrain+1:end);

2. Preparing training data and response sequences by shifting data by one time step, such as for data(t) the response will be data(t+1)

XTrain = dataTrain(1:end-1);
YTrain = dataTrain(2:end);

3. Preparing the network and training hyperparameters, then train the network using training data and training responses

numFeatures = 1;
numResponses = 1;
numHiddenUnits = 200;
layers = [ ...
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits)
    fullyConnectedLayer(numResponses)
    regressionLayer];
options = trainingOptions('adam', ...
    'MaxEpochs',250, ...
    'GradientThreshold',1, ...
    'InitialLearnRate',0.005, ...
    'Verbose',0, ...
    'Plots','training-progress');
net = trainNetwork(XTrain,YTrain,layers,options);

3. Now you can forecast 1, 2, 3 or 4 steps ahead using predictAndUpdateState function, since you use predicted values to update the network state and you don’t use actual values contained in dataTest for this, you can make predictions on any time step number

net = predictAndUpdateState(net,XTrain);
[net,YPred] = predictAndUpdateState(net,YTrain(end));
stepsAhead = 4; % you can use 1,2,3,4 on any value of steps ahead
for i = 2:stepsAhead+1
    [net,YPred(:,i)] = predictAndUpdateState(net,YPred(:,i-1));
end