How to use LSTM and CNN to handle a regression problem?
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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
lgraph = layerGraph(layers1);
lgraph = connectLayers(lgraph,'fold/miniBatchSize','unfold/miniBatchSize');
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!
H Sanchez on 30 Apr 2021
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
Abolfazl Nejatian on 10 Dec 2020
Edited: KSSV on 7 Aug 2022
i have written a prediction code that uses CNNs and LSTM to forecast future values.
please visit my Mathworks page,
Raunak Gupta on 19 Jul 2020
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.