Forecasting TAIFEX based on fuzzy time series and particle swarm optimization

The TAIFEX (Taiwan Stock Index Futures) forecasting problem has attracted some researchers’ attention in the past decades. Several forecast methods for the TAIFEX forecasting based either on the statistic theorems have been proposed, but their results are not satisfied. Fuzzy time series is used to doing forecasting but the forecasted accuracy still needs to be improved. In this paper we present a new hybrid forecast method to solve the TAIFEX forecasting problem based on fuzzy time series and particle swarm optimization. The experimental results show that the new proposed forecast model is better than any existing fuzzy forecast models and is more precise than four famous statistic forecast models.