Time series forecasting for stock market prediction through data discretization by fuzzistics and rule generation by rough set theory

Abstract

Data discretization is a preprocessing technique to mine essential information from the pool of information. It is also essential to generate rules from the processed data after mining information. In this paper, a hybrid approach is proposed to forecast time series of stock price by using data discretization based on fuzzistics (Mendel, 2007 [24]; Liu and Mendel, 2008), where cumulative probability distribution approach (CPDA) is used to get the intervals for the linguistic values. First order fuzzy rule generation and reduction of rule sets by rough set theory have been performed. Thereafter, forecasting of the time series data is computed from defuzzification using reduced rule base and its historical evidences. Proposed approach is applied on stock index closing price of three time series data (BSE, NYSE, and TAIEX) as experimental data sets and the results show that the method is more effective than its counter parts.