A BestMatch Forecasting Model for HighOrder Fuzz

Authors: YiChung Cheng ShengTun Li

Publish Date: 2013

Volume: , Issue: , Pages: 331-345

PDF Link

Abstract

An area of Fuzzy time series has attracted increasing interest in the past decade since Song and Chissom’s pioneering work and Chen’s milestone study Various enhancements and generalizations have been subsequently proposed including highorder fuzzy time series One of the key steps in the Chen’s framework is to derive fuzzy relationships existing in a fuzzy time series and to encode the relationships as IFTHEN production rules A generic exactmatch strategy is then applied to the forecasting process However the uncertainty and fuzziness characteristics inherent to the fuzzy relationships tend to be overlooked due to the nature of the matching strategies This omission could lead to inferior forecasting outcomes particularly in the case of highorder fuzzy time series In this study to overcome this shortcoming we propose a bestmatch strategy forecasting method based on the fuzzy similarity measure The experiments concerning Taiwan Weighted Stock Index and Dow Jones Industrial Average are reported We show the effectiveness of the model by running some comparative analysis using some models wellknown in the literature

Keywords: