A Type 2 fuzzy time series model for stock index forecasting

Kunhuang Huarng and Hui-Kuang Yu

Physica A: Statistical Mechanics and its Applications, 2005, vol. 353, issue C, 445-462

Abstract: Most conventional fuzzy time series models (Type 1 models) utilize only one variable in forecasting. Furthermore, only part of the observations in relation to that variable are used. To utilize more of that variable's observations in forecasting, this study proposes the use of a Type 2 fuzzy time series model. In such a Type 2 model, extra observations are used to enrich or to refine the fuzzy relationships obtained from Type 1 models and then to improve forecasting performance. The Taiwan stock index, the TAIEX, is used as the forecasting target. The study period extends over the 2000–2003 period. The TAIEX from January to October in each year is used for the estimation, while that covering November and December is used for forecasting. The empirical analyses show that Type 2 model outperforms Type 1 model.

Keywords: Fuzzy time series; Intersection; Stock index; Type 1; Type 2; Union (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:353:y:2005:i:c:p:445-462

DOI: 10.1016/j.physa.2004.11.070

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