• ISSN: 2301-3567
    • Frequency: Quarterly (2013-2014); Monthly (Since 2015)
    • DOI: 10.18178/JOEBM
    • Editor-in-Chief: Prof. Eunjin Hwang
    • Executive Editor: Ms. Chloe Wu
    • Abstracting/ Indexing: Engineering & Technology Library,  Electronic Journals Library, Ulrich's Periodicals Directory, MESLibrary, Google Scholar, Crossref, and ProQuest.
    • E-mail: joebm@ejournal.net
JOEBM 2015 Vol.3(3): 356-359 ISSN: 2301-3567
DOI: 10.7763/JOEBM.2015.V3.209

A Hybrid Model Based on ANFIS and Empirical Mode Decomposition for Stock Forecasting

Liang-Ying Wei
Abstract—Time series forecasting is an important and widely interesting topic in the research of system modeling and stock price forecasting is the most important research issues in time series forecasting. Accurate stock price forecasting is regarded as a challenging task of the financial time series forecasting process., This paper proposes a hybrid time-series adaptive network based fuzzy inference system (ANFIS) model based on empirical mode decomposition (EMD) to forecast stock price for Taiwan stock exchange capitalization weighted stock index (TAIEX). In order to evaluate the forecasting performances, the proposed model is compared with autoregressive (AR) model, ANFIS model and support vector regression (SVR) model. The experimental results show that the proposed model is superior to the listing models in terms of root mean squared error (RMSE).

Index Terms—Adaptive network based fuzzy inference system (ANFIS), empirical mode decomposition (EMD), TAIEX forecasting.

Liang-Ying Wei is with the Department of Information Management, Yuanpei University, 306 Yuanpei Street, Hsin Chu 30015, Taiwan.(e-amil: lywei@mail.ypu.edu.tw).

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Cite: Liang-Ying Wei, "A Hybrid Model Based on ANFIS and Empirical Mode Decomposition for Stock Forecasting," Journal of Economics, Business and Management vol. 3, no. 3, pp. 356-359, 2015.

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