Fuzzy time-series based on adaptive expectation model for TAIEX forecasting
Time-series models have been used to make predictions in the areas of stock price forecasting, academic enrollment and weather, etc. However, in stock markets, reasonable investors will modify their forecasts based on recent forecasting errors. Therefore, we propose a new fuzzy time-series model which incorporates the adaptive expectation model into forecasting processes to modify forecasting errors. Using actual trading data from Taiwan Stock Index (TAIEX) and, we evaluate the accuracy of the proposed model by comparing our forecasts with those derived from Chens [Chen, S. M. (1996). Forecasting enrollments based on fuzzy time-series, Fuzzy Sets and Systems, 81, 311319] and Yus [Yu, Hui-Kuang. (2004). Weighted fuzzy time-series models for TAIEX forecasting. Physica A, 349, 609624] models. The comparison results indicate that our model surpasses in accuracy those suggested by Chen and Yu.
adaptive expectation model taiex forecasting fuzzy time-series model fuzzy linguistic variable