Multi-SVR based fuzzy modeling method for non-stationary time series

Shu Kuan Lin; Li Jia Zhi; Shao Min Zhang; Jian Zhong Qiao; Guo Ren Wang; Ge Yu

Multi-SVR based fuzzy modeling method for non-stationary time series

Shu Kuan Lin^*
, Li Jia Zhi
, Shao Min Zhang
, Jian Zhong Qiao
, Guo Ren Wang
, Ge Yu

^*Corresponding author for this work

Northeastern University China

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

A new approach for modeling non-stationary time series was introduced in this paper. Combine the fuzzy segmentation which was proposed by Janos Abonyi with Support Vector Machines (SVMs). Firstly, a modified Support Vector Regression (SVR) was proposed; Secondly, fuzzy segment information was combined with SVR by heuristic weighting method; Thirdly, we discussed a model based on multi-SVR. Experimental results show that the method proposed in this paper has great practical values for non-stationary time series modeling.

Original language	English
Pages (from-to)	1929-1932
Number of pages	4
Journal	Tien Tzu Hsueh Pao/Acta Electronica Sinica
Volume	34
Issue number	10
Publication status	Published - Oct 2006
Externally published	Yes

Keywords

Fuzzy segmentation
Heuristic weighting method
Multi support vector regression
Non-stationary time series

Cite this

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title = "Multi-SVR based fuzzy modeling method for non-stationary time series",

abstract = "A new approach for modeling non-stationary time series was introduced in this paper. Combine the fuzzy segmentation which was proposed by Janos Abonyi with Support Vector Machines (SVMs). Firstly, a modified Support Vector Regression (SVR) was proposed; Secondly, fuzzy segment information was combined with SVR by heuristic weighting method; Thirdly, we discussed a model based on multi-SVR. Experimental results show that the method proposed in this paper has great practical values for non-stationary time series modeling.",

keywords = "Fuzzy segmentation, Heuristic weighting method, Multi support vector regression, Non-stationary time series",

author = "Lin, \{Shu Kuan\} and Zhi, \{Li Jia\} and Zhang, \{Shao Min\} and Qiao, \{Jian Zhong\} and Wang, \{Guo Ren\} and Ge Yu",

year = "2006",

month = oct,

language = "English",

volume = "34",

pages = "1929--1932",

journal = "Tien Tzu Hsueh Pao/Acta Electronica Sinica",

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publisher = "Chinese Institute of Electronics",

number = "10",

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T1 - Multi-SVR based fuzzy modeling method for non-stationary time series

AU - Lin, Shu Kuan

AU - Zhi, Li Jia

AU - Zhang, Shao Min

AU - Qiao, Jian Zhong

AU - Wang, Guo Ren

AU - Yu, Ge

PY - 2006/10

Y1 - 2006/10

N2 - A new approach for modeling non-stationary time series was introduced in this paper. Combine the fuzzy segmentation which was proposed by Janos Abonyi with Support Vector Machines (SVMs). Firstly, a modified Support Vector Regression (SVR) was proposed; Secondly, fuzzy segment information was combined with SVR by heuristic weighting method; Thirdly, we discussed a model based on multi-SVR. Experimental results show that the method proposed in this paper has great practical values for non-stationary time series modeling.

AB - A new approach for modeling non-stationary time series was introduced in this paper. Combine the fuzzy segmentation which was proposed by Janos Abonyi with Support Vector Machines (SVMs). Firstly, a modified Support Vector Regression (SVR) was proposed; Secondly, fuzzy segment information was combined with SVR by heuristic weighting method; Thirdly, we discussed a model based on multi-SVR. Experimental results show that the method proposed in this paper has great practical values for non-stationary time series modeling.

KW - Fuzzy segmentation

KW - Heuristic weighting method

KW - Multi support vector regression

KW - Non-stationary time series

UR - https://www.scopus.com/pages/publications/33846105059

M3 - Article

AN - SCOPUS:33846105059

SN - 0372-2112

VL - 34

SP - 1929

EP - 1932

JO - Tien Tzu Hsueh Pao/Acta Electronica Sinica

JF - Tien Tzu Hsueh Pao/Acta Electronica Sinica

IS - 10

ER -

Multi-SVR based fuzzy modeling method for non-stationary time series

Abstract

Keywords

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