stacked LSTm Code for time series forecasting

Can anyone guide me how to write code for stacked lstm in the below code:

layers = [ ...
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits)
    fullyConnectedLayer(numResponses)
    regressionLayer];

Answer 1

[Abhishek Gupta]

Hi,

As per my understanding, you want to define an LSTM model comprise of multiple LSTM layers. One can perform this task as follows: -

layers = [ ...
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits1)
    lstmLayer(numHiddenUnits2)
    fullyConnectedLayer(numClasses)
    regressionLayer];

Referring to the following documentation for more information: -

• Deeper LSTM Networks

Answer 2

[nahed zemouri]

hello

ihave a question about times series forecasting using LSTM

this is my program and i have this error please help me

close all; clear; clc;

load Tucson;

Input1= GHI_hour_Y1;

Input2=GHI_hour_Y2;

Pmpp=[Input1, Input2];%%%%%vecteur

%Output data

for i=1:length(Pmpp)

if Pmpp(i) <0

Pmpp(i)=0;

else

Pmpp(i)=Pmpp(i);

end

end

%Partition the training and test data.

numTimeStepsTrain = floor(0.7*numel(Pmpp));

dataTrain_S = Pmpp(1:numTimeStepsTrain+1);

dataTest_S = Pmpp(numTimeStepsTrain+1:end);

dataTrain_Pmpp = Pmpp(1:numTimeStepsTrain+1);

dataTest_Pmpp = Pmpp(numTimeStepsTrain+1:end);

%%Standardize Data

mu_S = mean(dataTrain_S);

sig_S = std(dataTrain_S);

dataTrainStandardized_S = (dataTrain_S - mu_S) / sig_S;

mu_Pmpp = mean(dataTrain_Pmpp);

sig_Pmpp = std(dataTrain_Pmpp);

dataTrainStandardized_Pmpp = (dataTrain_Pmpp - mu_Pmpp) / sig_Pmpp;

%%Prepare Predictors and Responses

XTrain = dataTrainStandardized_S(1:end-1);

XTrain = XTrain';

YTrain = dataTrainStandardized_Pmpp(1:end-1);

YTrain = YTrain';

for i=1:length(YTrain)

if YTrain(i) <0

YTrain(i)=0;

else

YTrain(i)=YTrain(i);

end

end

%%Define LSTM Network Architecture

numFeatures = 1;

numResponses = 1;

numHiddenUnits = 2;

layers = [ ...

sequenceInputLayer(numFeatures)

lstmLayer(numHiddenUnits)

fullyConnectedLayer(numResponses)

regressionLayer];

options = trainingOptions('adam', ...

'MaxEpochs',2, ...

'GradientThreshold',1, ...

'InitialLearnRate',0.005, ...

'LearnRateSchedule','piecewise', ...

'LearnRateDropPeriod',125, ...

'LearnRateDropFactor',0.2, ...

'Verbose',0, ...

'Plots','training-progress');

%%Train LSTM Network

net = trainNetwork(XTrain',YTrain',layers,options);

return

%%Forecast Future Time Steps

%Standardize the test data using the same parameters as the training data.

net = predictAndUpdateState(net,XTrain);

Error using nnet.internal.cnn.util.NetworkDataValidator/assertValidSequenceInput

(line 493)

The prediction sequences are of feature dimension 4949 but the input layer expects

sequences of feature dimension 1.

[Abhishek Gupta]

To get a quick answer, I suggest you create a new question rather than posting it as an answer here.

For more information: -

https://www.mathworks.com/matlabcentral/answers/6200-tutorial-how-to-ask-a-question-on-answers-and-get-a-fast-answer