Fuzzy Semiparametric Support Vector Regression for Seasonal Time Series Analysis

- Journal title : Communications for Statistical Applications and Methods
- Volume 16, Issue 2, 2009, pp.335-348
- Publisher : The Korean Statistical Society
- DOI : 10.5351/CKSS.2009.16.2.335

Title & Authors

Fuzzy Semiparametric Support Vector Regression for Seasonal Time Series Analysis

Shim, Joo-Yong; Hwang, Chang-Ha; Hong, Dug-Hun;

Shim, Joo-Yong; Hwang, Chang-Ha; Hong, Dug-Hun;

Abstract

Fuzzy regression is used as a complement or an alternative to represent the relation between variables among the forecasting models especially when the data is insufficient to evaluate the relation. Such phenomenon often occurs in seasonal time series data which require large amount of data to describe the underlying pattern. Semiparametric model is useful tool in the case where domain knowledge exists about the function to be estimated or emphasis is put onto understandability of the model. In this paper we propose fuzzy semiparametric support vector regression so that it can provide good performance on forecasting of the seasonal time series by incorporating into fuzzy support vector regression the basis functions which indicate the seasonal variation of time series. In order to indicate the performance of this method, we present two examples of predicting the seasonal time series. Experimental results show that the proposed method is very attractive for the seasonal time series in fuzzy environments.

Keywords

Fuzzy regression;seasonal time series;semiparametric model;support vector regression;

Language

English

Cited by

1.

서포트벡터기계를 이용한 VaR 모형의 결합,김용태;심주용;이장택;황창하;

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