NDLI: Online multi-step-ahead time series prediction based on LSSVR using UKF with sliding-windows

Content Provider	IEEE Xplore Digital Library
Author	Xiaoyong Liu Huajing Fang
Copyright Year	2015
Description	Author affiliation: Sch. of Autom., Huazhong Univ. of Sci. & Technol., Wuhan, China (Xiaoyong Liu; Huajing Fang)
Abstract	Accurate multi-step-ahead prediction over long future horizons posts great challenges for the application of time series prediction. A novel online multi-step-ahead prediction method based on least squares support vector regression (LSSVR) is proposed in this paper. Taken the superiorities of using sliding-windows to reduce largely computation burden and implementing LSSVR model updating by Unscented Kalman Filter (UKF) into consideration, the proposed method not only can construct online predicted model in much fewer training data (such as the size of original training data set required is only the sum of embedding dimension corresponding to phase-space-reconstruction and the length of sliding-windows), but also has the better accuracy over multi-step-ahead prediction. When the prediction horizon reached the predefined step p in the process of predicting, model parameters consisted of kernel width σ, support values $\{α_{k}\}_{k=1}^{L}$ and bias term b are updated by new arrived measurements and UKF. Finally, several simulations are provided to show the validity and applicability of the proposed method.
Starting Page	4121
Ending Page	4126
File Size	172994
Page Count	6
File Format	PDF
e-ISBN	9781479970179
DOI	10.1109/CCDC.2015.7162646
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Computational modeling Time series analysis Training data Predictive models Online Multi-step-ahead Prediction Unscented Kalman Filter Data models Mathematical model Sliding-Windows LSSVR
Content Type	Text
Resource Type	Article

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