Prediction for Chaotic Time Series-Based AE-CNN and Transfer Learning

Baogui Xin and Wei Peng

Complexity, 2020, vol. 2020, 1-9

Abstract:

It has been a hot and challenging topic to predict the chaotic time series in the medium-to-long term. We combine autoencoders and convolutional neural networks (AE-CNN) to capture the intrinsic certainty of chaotic time series. We utilize the transfer learning (TL) theory to improve the prediction performance in medium-to-long term. Thus, we develop a prediction scheme for chaotic time series-based AE-CNN and TL named AE-CNN-TL. Our experimental results show that the proposed AE-CNN-TL has much better prediction performance than any one of the following: AE-CNN, ARMA, and LSTM.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:2680480

DOI: 10.1155/2020/2680480

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