Stock markets forecasting based on fuzzy time series model

This paper is aimed at improving the forecasting accuracy with correcting two deficiencies, subintervals failing to well represent the data distribution structures and a single antecedent factor in the fuzzy relationships in current fuzzy time series models. First, the universe of discourse is partitioned into subintervals with the midpoints of two adjacent cluster centers generated by the fuzzy clustering method as their endpoints. And the sub-intervals are employed to fuzzify the time series into fuzzy time series. Then, the fuzzy time series model with multi-factors high-order fuzzy relationships is built up to forecast the stock markets. Finally, the model we produced is used to forecast the daily Shanghai Stock Exchange Composite index and Shenzhen Stock Exchange Component index, respectively. The results show that the model do improve the prediction accuracy compared with the benchmark model.