TAIFEX and KOSPI 200 forecasting based on two-factors high-order fuzzy time series and particle swarm optimization

Authors:
Jin-Il Park;Dae-Jong Lee;Chang-Kyu Song;Myung-Geun Chun
Affiliations:
Department of Electrical and Computer Engineering, Chungbuk National University, Gaeshin-dong, 12, Cheongju 361-763, Republic of Korea;Department of Electrical and Computer Engineering, Chungbuk National University, Gaeshin-dong, 12, Cheongju 361-763, Republic of Korea;CBNU BK21 Chungbuk Information Technology Center, Chungbuk National University, Gaeshin-dong, 12, Cheongju 361-763, Republic of Korea;Department of Electrical and Computer Engineering, Chungbuk National University, Gaeshin-dong, 12, Cheongju 361-763, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Since the fuzzy time series forecasting methods provide a powerful framework to cope with vague or ambiguous problems, they have been widely used in real applications. The forecasting accuracy of these methods usually, however, depend on their universe of discourse and the length of intervals. So, we present a new forecasting method using two-factors high-order fuzzy time series and particle swarm optimization (PSO) for increasing the forecasting accuracy. To show the effectiveness of the proposed method, we applied our method for the Taiwan futures exchange (TAIFEX) forecasting and the Korea composite price index (KOSPI) 200 forecasting. The results show better forecasting accuracy than previous methods.