A Markov-fuzzy Combination Model For Stock Market Forecasting

被引：0

作者：

Dao Xuan Ky ^{[1
]}

Luc Tri Tuyen ^{[2
]}

机构：

[1] Dept Informat & Commun Ninh Thuan Prov, Nguyen Trai 17, Phan Rang Thap Cham 63, Vietnam

[2] Vietnam Acad Sci & Technol, Inst Informat Technol, Hoang Quoc Viet 18, Hanoi 10, Vietnam

来源：

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS & STATISTICS | 2016年 / 55卷 / 03期

关键词：

imakov chain; fuzzy time series; stock market; forecasting; hidden markov model;

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper presents a simple combination of Markov model and fuzzy time series model (called MC-fuzzy) for forecasting stock market data. The fuzzy time series model is used to partition the dataspace into states and also solves the fuzzy data in stock index future price. The Markov model then is used to identify data patterns and forecast future states. The appropriate fuzzy rule then defuzzies the data for forecast value. Experimental results of the proposed model show that the performance is better than other forecasting models such as, ARIMA, artificial neural network (ANN), Hidden Markov Model (HMM) -based models and equivalent to HMM-Fuzzy models.

引用

页码：109 / 121

页数：13

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