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

Abstract EN

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.