Multi step ahead forecasting with LSTM

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anurag kulshrestha 2020-2-7

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https://ww2.mathworks.cn/matlabcentral/answers/504175-multi-step-ahead-forecasting-with-lstm

评论： Raziye Ghasemi 2023-11-22

How to perform multi-step ahead forecasting with LSTM. I want to predict 2,3, and 4 time stesp ahead prediction with LSTM?

Please help. Thanks in advance.

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Nikolaos Efkarpidis 2021-1-13

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Dear,
How did you finally perform multi-step ahead forecasting with LSTM and predictAndUpdateState ?
I am also struggling with this issue.
Best regards,
Nick 

Raziye Ghasemi 2023-11-22

How did you finally perform multi-step ahead forecasting with LSTM and predictAndUpdateState ? I am also struggling with this issue.

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Answer 1

adel adel 2020-5-25

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https://ww2.mathworks.cn/matlabcentral/answers/504175-multi-step-ahead-forecasting-with-lstm#answer_438498

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Forecasting is basicaly sequence-to-sequence regression, let suppos that your entire sequence is data,

1. You divide data into train and test parts, you can specify the proportion as you wish:

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