A hybrid predictor for time series prediction

Yen Ping Chen, Sheng Nan Wu, Jeen-Shing Wang

研究成果: Conference contribution

2 引文 (Scopus)

摘要

This paper presents a hybrid predictor for the CATS (Competition on Artificial Time Series) benchmark. Considering the time series as a sum of two components: the major trend and a residual series, we tackled the prediction problem by a hybrid predictor consisting of two models - a kernel regression model and a recurrent neuro-fuzzy model. The kernel regression model based on Gaussian function expansions was first applied to predict the major trend of the time series. The time series was sectioned into several data sets to obtain the best-fitting regression model. Subsequently, the recurrent neuro-fuzzy model associated with a learning algorithm was used to predict the dynamics of the residual series. The learning algorithm has been developed to construct a minimum size of the recurrent model in state-space representation. The best prediction results were presented and discussed.

原文English
主出版物標題2004 IEEE International Joint Conference on Neural Networks - Proceedings
頁面1597-1602
頁數6
2
DOIs
出版狀態Published - 2004
事件2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
持續時間: 2004 七月 252004 七月 29

Other

Other2004 IEEE International Joint Conference on Neural Networks - Proceedings
國家Hungary
城市Budapest
期間04-07-2504-07-29

指紋

Time series
Learning algorithms

All Science Journal Classification (ASJC) codes

  • Software

引用此文

Chen, Y. P., Wu, S. N., & Wang, J-S. (2004). A hybrid predictor for time series prediction. 於 2004 IEEE International Joint Conference on Neural Networks - Proceedings (卷 2, 頁 1597-1602) https://doi.org/10.1109/IJCNN.2004.1380196
Chen, Yen Ping ; Wu, Sheng Nan ; Wang, Jeen-Shing. / A hybrid predictor for time series prediction. 2004 IEEE International Joint Conference on Neural Networks - Proceedings. 卷 2 2004. 頁 1597-1602
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Chen, YP, Wu, SN & Wang, J-S 2004, A hybrid predictor for time series prediction. 於 2004 IEEE International Joint Conference on Neural Networks - Proceedings. 卷 2, 頁 1597-1602, 2004 IEEE International Joint Conference on Neural Networks - Proceedings, Budapest, Hungary, 04-07-25. https://doi.org/10.1109/IJCNN.2004.1380196

A hybrid predictor for time series prediction. / Chen, Yen Ping; Wu, Sheng Nan; Wang, Jeen-Shing.

2004 IEEE International Joint Conference on Neural Networks - Proceedings. 卷 2 2004. p. 1597-1602.

研究成果: Conference contribution

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AB - This paper presents a hybrid predictor for the CATS (Competition on Artificial Time Series) benchmark. Considering the time series as a sum of two components: the major trend and a residual series, we tackled the prediction problem by a hybrid predictor consisting of two models - a kernel regression model and a recurrent neuro-fuzzy model. The kernel regression model based on Gaussian function expansions was first applied to predict the major trend of the time series. The time series was sectioned into several data sets to obtain the best-fitting regression model. Subsequently, the recurrent neuro-fuzzy model associated with a learning algorithm was used to predict the dynamics of the residual series. The learning algorithm has been developed to construct a minimum size of the recurrent model in state-space representation. The best prediction results were presented and discussed.

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Chen YP, Wu SN, Wang J-S. A hybrid predictor for time series prediction. 於 2004 IEEE International Joint Conference on Neural Networks - Proceedings. 卷 2. 2004. p. 1597-1602 https://doi.org/10.1109/IJCNN.2004.1380196