A hybrid multi-order fuzzy time series for forecasting stock markets

  • Authors:

  • Hia Jong Teoh;Tai-Liang Chen;Ching-Hsue Cheng;Hsing-Hui Chu

  • Affiliations:

  • Department of Accounting &Information Technology, Ling Tung University, 1, Ling Tung Road, Nantun, Taichung 408, Taiwan, ROC;Department of Information Management and Communication, WenZao Ursuline College of Language, 900, Min-Tzu 1st Road Kaohsiung, 807 Taiwan, ROC;Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Touliu, Yunlin 640, Taiwan, ROC;Department of Accounting &Information Technology, Ling Tung University, 1, Ling Tung Road, Nantun, Taichung 408, Taiwan, ROC

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

Quantified Score

Hi-index 12.06

Visualization

Abstract

This paper proposes a hybrid model based on multi-order fuzzy time series, which employs rough sets theory to mine fuzzy logical relationship from time series and an adaptive expectation model to adjust forecasting results, to improve forecasting accuracy. Two empirical stock markets (TAIEX and NASDAQ) are used as empirical databases to verify the forecasting performance of the proposed model, and two other methodologies, proposed earlier by Chen and Yu, are employed as comparison models. Besides, to compare with conventional statistic method, the partial autocorrelation function and autoregressive models are utilized to estimate the time lags periods within the databases. Based on comparison results, the proposed model can effectively improve the forecasting performance and outperforms the listing models. From the empirical study, the conventional statistic method and the proposed model both have revealed that the estimated time lags for the two empirical databases are one lagged period.