Prediction for Chaotic Time Series-Based AE-CNN and Transfer Learning
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...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/2680480 |
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| _version_ | 1849683603756154880 |
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| author | Baogui Xin Wei Peng |
| author_facet | Baogui Xin Wei Peng |
| author_sort | Baogui Xin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d422c9c8d7094a8ca9ea2e1beedaf3e8 |
| institution | DOAJ |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-d422c9c8d7094a8ca9ea2e1beedaf3e82025-08-20T03:23:47ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/26804802680480Prediction for Chaotic Time Series-Based AE-CNN and Transfer LearningBaogui Xin0Wei Peng1College of Economics and Management, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Economics and Management, Shandong University of Science and Technology, Qingdao 266590, ChinaIt 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.http://dx.doi.org/10.1155/2020/2680480 |
| spellingShingle | Baogui Xin Wei Peng Prediction for Chaotic Time Series-Based AE-CNN and Transfer Learning Complexity |
| title | Prediction for Chaotic Time Series-Based AE-CNN and Transfer Learning |
| title_full | Prediction for Chaotic Time Series-Based AE-CNN and Transfer Learning |
| title_fullStr | Prediction for Chaotic Time Series-Based AE-CNN and Transfer Learning |
| title_full_unstemmed | Prediction for Chaotic Time Series-Based AE-CNN and Transfer Learning |
| title_short | Prediction for Chaotic Time Series-Based AE-CNN and Transfer Learning |
| title_sort | prediction for chaotic time series based ae cnn and transfer learning |
| url | http://dx.doi.org/10.1155/2020/2680480 |
| work_keys_str_mv | AT baoguixin predictionforchaotictimeseriesbasedaecnnandtransferlearning AT weipeng predictionforchaotictimeseriesbasedaecnnandtransferlearning |