Forecasting short-term trends of stock markets based on fuzzy frequent pattern tree

Authors:
Defu Zhang;Bo Wu;Xian Hua;Yangbin Yang
Affiliations:
Department of Computer Science, Xiamen University, Xiamen, China;Department of Computer Science, Xiamen University, Xiamen, China;Department of Computer Science, Xiamen University, Xiamen, China;Department of Computer Science, Xiamen University, Xiamen, China
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Year:
2010

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Abstract

Stock forecasting is a non-linear financial time series forecasting problem. Stock index contains tremendous noise and is affected by numerous factors. Fuzzy time series takes advantage of such problems. In this paper, a novel model based on the fuzzy frequent pattern tree (FFPT) is proposed to forecast short-term trends of stock markets. Fuzzy frequent pattern tree is a combination of fuzzy set theory and frequent pattern tree. Frequent pattern tree is a highly compressed data structure store the information of association rules to be mined. In this paper, an FFPT is built using fuzzy stock time series. Then we forecast short-term trends by a new method called FFPTSearch. And stock data from several famous stock markets is picked up to evaluate the effectiveness of our model. Computational results indicate it works well.