Two-factor high-order fuzzy-trend FTS model based on BSO-FCM and improved KA for TAIEX stock forecasting

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

8 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)1429-1446
Journal / PublicationNonlinear Dynamics
Volume94
Issue number2
Online published27 Jun 2018
Publication statusPublished - Oct 2018

Abstract

Fuzzy time series has been an effective and attractive forecasting model for solving the problem of stock index forecasting. In particular, fuzzy-trend fuzzy time series models have been proposed recently to address complex cases and perform well in terms of forecasting accuracy. Nonetheless, they have just explored the two-factor second-order forecasting but cannot satisfy the complex stock system. In this study, we proposed a new two-factor high-order fuzzy-trend fuzzy time series model to explore the more complex situation on the TAIEX stock index forecasting. We presented the backtracking search optimization-fuzzy c-means method to obtain the optimal intervals of the data sets. In addition, an improved kidney-inspired algorithm is employed to integrate the high-order forecasting values. The proposed model shows outstanding forecasting accuracy than the benchmark methods on the TAIEX. Besides, we combined two other stock indexes (NASDAQ and the Dow Jones) as the secondary factors, respectively. It provides a useful method for two-factor high-order fuzzy-trend fuzzy time series stock index forecasting.

Research Area(s)

  • Backtracking search optimization, Fuzzy-trend fuzzy time series, Kidney-inspired algorithm, TAIEX forecast, Two-factor high-order