stacked LSTm Code for time series forecasting

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NN
NN el 11 de Dic. de 2020
Respondida: NGR MNFD el 2 de Jul. de 2021
Can anyone guide me how to write code for stacked lstm in the below code:
layers = [ ...
sequenceInputLayer(numFeatures)
lstmLayer(numHiddenUnits)
fullyConnectedLayer(numResponses)
regressionLayer];

Respuestas (3)

Abhishek Gupta
Abhishek Gupta el 15 de Dic. de 2020
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: -

nahed zemouri
nahed zemouri el 11 de En. de 2021
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.

NGR MNFD
NGR MNFD el 2 de Jul. de 2021
Hello . I hope you have a good day. I sent the article to your service. I implemented the coding part in the MATLAB software, but to implement my network, two lines of setlayers, training MATLAB 2014 give me an error. What other function do you think I should replace? Do you think the codes I wrote are correct?( I used gait-in-neurodegenerative-disease-database in physionet website.) Thanks a lot

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