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

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Hui Li 2020-7-14

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评论： rohini sharma 2022-10-20

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!

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Davey Gregg 2022-4-6

编辑：Davey Gregg 2022-4-6

How are you arranging the data into predictors and responses? I'm trying to do something similar and I just keep getting the "Invalid training data." error.

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Answer 1

H Sanchez 2021-4-30

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To Whoever is looking for a CNN-RNN

I have created a simple template for hybrids cnn-rnn for time series forecasting. https://www.mathworks.com/matlabcentral/fileexchange/91360-time-series-forecasting-using-hybrid-cnn-rnn

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Muhammad Waqar 2021-12-22

This is an excellent contribution.

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Answer 2

Abolfazl Nejatian 2020-12-10

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编辑：KSSV 2022-8-7

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

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Abolfazl Nejatian 2021-12-13

yes, but it needs some minor changes.

Imola Fodor 2022-3-3

what are the changes we need for the real time prediction? i have developed a regression model (for sysid) 1dcnn + LSTM on 1500 timesteps, and it works well, but when giving an input of 500 it is performing badly..i suppose the model needs the full input sequence to perform well, which is not what i would need

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Answer 3

Raunak Gupta 2020-7-19

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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.

Sequence to Sequence regression.

Time Series Forecasting

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Nazila Pourhajy 2021-11-3

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

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.

rohini sharma 2022-10-20

is lstm can be applied in 2018 a matlab please reply

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