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Local Prediction of Chaotic Time Series Based on Polynomial Coefficient Autoregressive Model
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-07-21
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
We apply the polynomial function to approximate the functional coefficients of the state-dependent autoregressive model for chaotic time series prediction.
We present a novel local nonlinear model called local polynomial coefficient autoregressive prediction (LPP) model based on the phase space reconstruction.
The LPP model can effectively fit nonlinear characteristics of chaotic time series with simple structure and have excellent one-step forecasting performance.
We have also proposed a kernel LPP (KLPP) model which applies the kernel technique for the LPP model to obtain better multistep forecasting performance.
The proposed models are flexible to analyze complex and multivariate nonlinear structures.
Both simulated and real data examples are used for illustration.
American Psychological Association (APA)
Su, Liyun& Li, Chenlong. 2015. Local Prediction of Chaotic Time Series Based on Polynomial Coefficient Autoregressive Model. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-14.
https://search.emarefa.net/detail/BIM-1075001
Modern Language Association (MLA)
Su, Liyun& Li, Chenlong. Local Prediction of Chaotic Time Series Based on Polynomial Coefficient Autoregressive Model. Mathematical Problems in Engineering No. 2015 (2015), pp.1-14.
https://search.emarefa.net/detail/BIM-1075001
American Medical Association (AMA)
Su, Liyun& Li, Chenlong. Local Prediction of Chaotic Time Series Based on Polynomial Coefficient Autoregressive Model. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-14.
https://search.emarefa.net/detail/BIM-1075001
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1075001