How to use LSTM and CNN to handle a regression problem?

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Hui Li
Hui Li on 14 Jul 2020
Commented: Abolfazl Nejatian on 25 Nov 2021 at 10:38
Hi, everyone!
I am working on a solar power prediction problem. The inputs of the network are some kinds of meteological data, and the outputs are multiple time-series solar power curves. I want to build a neural network combining LSTM and CNN to realize this function. I build a network without error like this:
layers1 = [...
sequenceInputLayer([25 168 1],'Name','input') % 25 is the number of feature dimension of meteological data, and 168 is the length of time series
sequenceFoldingLayer('Name','fold')
convolution2dLayer(5,1,'Padding','same','WeightsInitializer','he','BiasInitializer','zeros','Name','conv');
reluLayer('Name','relu')
sequenceUnfoldingLayer('Name','unfold')
flattenLayer('Name','flatten')
gruLayer(512,'OutputMode','sequence','Name','gru')
fullyConnectedLayer(25,'Name','fc2')
regressionLayer('Name','output')
];
lgraph = layerGraph(layers1);
lgraph = connectLayers(lgraph,'fold/miniBatchSize','unfold/miniBatchSize');
analyzeNetwork(lgraph);
However, the flattenLayer destory the time series, and the training cannot be finished.
Therefore, is there any solution about this problem? Or is there any other correct network can realize the same function?
Thanks in advance for your time and kindly help!

Answers (3)

Abolfazl Nejatian
Abolfazl Nejatian on 10 Dec 2020
Dear Gupta,
i have written a prediction code that uses CNNs and LSTM to forecast future values.
please visit my Mathworks page,
https://www.mathworks.com/matlabcentral/fileexchange/69506-time-series-prediction
  2 Comments
Abolfazl Nejatian
Abolfazl Nejatian on 25 Nov 2021 at 10:38
dear Hiren,
there are several version of codes on that link.
as i remember, MLP, LSTM, CNN, LSTM with Bayesian Optimization, Hybrid CNN_LSTM, Hybrid CNN_LSTM with Bayesian Optimization exist.
so the combination of CNN_LSTM exist on my mathwork acocunt

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Raunak Gupta
Raunak Gupta on 19 Jul 2020
Hi,
I am unable to understand what exactly you are doing with input and output of the network, but I think its related to either sequence to sequence regression or time series forecasting. You may follow below mentioned examples for both cases and see if it matches with your application.
  3 Comments
Nazila Pourhajy
Nazila Pourhajy on 3 Nov 2021 at 8:04
Hi. I have a question about LSTM. My problem about sequence to sequence reression. I have input matrix(1000*8) and I want to predict a price with this input matrix. output is a column that is a price. I train LSTM with input matrix and I predict LSTM with datatest(50*8). But I want to calculate error of LSTM and I use predict function for 10 times with the same datatest and I get predicted value every time that are not different from Previous time. How I calculate RMSE for LSTM with some predict function.Here is may code:
function LSTM_net(data,dataTest,filename,range,date,varargin)
%--------------80% of data for train and 20% for validation----------------
out_day=cell.empty;
index=size(data{1,1},1)*0.8;
findex=round(index,0);
dataTrain=data(1:findex,:);
dataval=data(findex+1:end,:);
%-----------------Normalization of training/validation data----------------
dataTrain(isnan(dataTrain))=0;
dataval(isnan(dataval))=0;
dataTrain=rescale(dataTrain,0,1);
dataval=rescale(dataval,0,1);
YTrain = dataTrain(:,end)';
XTrain = dataTrain(:,1:end-1)';
XTrain = num2cell(XTrain,1);
YTrain = num2cell(YTrain,1);
yval= dataval(:,end)';
xval = dataval(:,1:end-1)';
xval = num2cell(xval,1);
yval = num2cell(yval,1);
%-----------------------Define Network Architecture------------------------
numResponses = size(YTrain{1},1);
featureDimension = size(XTrain{1},1);
numHiddenUnits = 15;
layers = [ ...
sequenceInputLayer(featureDimension)
lstmLayer(numHiddenUnits,'OutputMode','sequence')
dropoutLayer(0.5) %%0.5
fullyConnectedLayer(numResponses)
regressionLayer];
maxepochs = 500;
options = trainingOptions('sgdm', ...
'MaxEpochs',maxepochs, ...
'InitialLearnRate',0.01, ...
'L2Regularization',0.001,...
'ValidationData',{xval,yval},...
'ValidationPatience',5,...
'ValidationFrequency',10);
%---------------------------------set test data----------------------------
dataTest=rescale(dataTest,0,1);
YTest = dataTest{k,1}(:,end)';
XTest = dataTest{k,1}(:,1:end-1)';
XTest = num2cell(XTest,1);
YTest = num2cell(YTest,1);
%---------------------------------Train the Network------------------------
out_net=single.empty;
%load('net_checkpoint__110__2021_11_01__10_49_03_555','net');
[net1,info] = trainNetwork(XTrain,YTrain,layers,options);
for i=1:10
YPred = predict(net1,XTest);
net1 = resetState(net1);
%figure;
%subplot(2,1,1);
y1 = (cell2mat(YPred(1:end, 1:end)));
%plot(y1);
%title('Forcasted');
%subplot(2,1,2);
y2 = (cell2mat(YTest(1:end, 1:end))');
%plot(y2);
%title('Observed');
y1(isnan(y1))=0;
y2(isnan(y2))=0;
%----------------------------calculate MAE,RMSE,MAPE-----------------------
out_net(i,1)=mean(info.TrainingRMSE(1,:),2);
out_net(i,2)=mean(abs(y1-y2)); %MAE
out_net(i,3)=mean(abs((y1-y2)/mean(y1))); %MAPE
out_net(i,4) = sqrt(mean((y1-y2).^2)); %RMSE
if size(varargin,1)==1 %for plot regression
predict_y(:,i)=y1;
end
end
end
I trained LSTM one time and predict it for 10 times and I get the same YPred answer every time.Is my code true?Please help me.

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