Time Series Forecasting Using Hybrid CNN - RNN

バージョン 1.0.13 (566 KB) 作成者: H Sanchez
A hybrid convolutional neural network - recurrent neural network (RNN) for time series prediction is implemented.
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更新 2021/5/27

ライセンスの表示

This example aims to present the concept of combining a convolutional neural network (CNN) with a recurrent neural network (RNN) to predict the number of chickenpox cases based on previous months.
The CNN is an excellent net for feature extractions while a RNN have proved its ability to predict values in sequence-to-sequence series. At each time step the CNN extracts the main features of the sequence while the RNN learn to predict the next value on the next time step.
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引用

H Sanchez (2024). Time Series Forecasting Using Hybrid CNN - RNN (https://www.mathworks.com/matlabcentral/fileexchange/91360-time-series-forecasting-using-hybrid-cnn-rnn), MATLAB Central File Exchange. に取得済み.

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作成: R2020b
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バージョン 公開済み リリース ノート
1.0.13

A hybrid Xception - RNN network is included in this new version. Notice Xception requires high computing power for training. Using last Gen AMD processor and last gen Nvidia 3080 may take around 30 mins to train the network.
1.0.12

A feedforward network options for prediction has been included. It is shown the process of data preparation process for a univariate time series forecasting.

1.0.11

A hybrid ResNet50-RNN was included in this example.
The example includes horizon to predict the values beyond the testing data. It now predicts unobservable values beyond the limit of the testing sequence.
Better documented and explained.
1.0.2

A hybrid ResNet50-RNN was included in this example.
The example includes horizon to predict the values beyond the testing data. It now predicts unobservable values beyond the limit of the testing sequence.
Better documented and explained.
1.0.1

A hybrid ResNet50-RNN was included in this example.
The example includes horizon to predict the values beyond the testing data. It now predicts unobservable values beyond the limit of the testing sequence.
Better documented and explained.

1.0.0