Modeling Fuzzy Time Series with Multiple Observations

The application of fuzzy time series models to forecasting has been drawing a great amount of attention. To provide a more sophisticated model to handle real world problems thus becomes important. This study intends to model fuzzy time series with multiple observations at a single time point. The proposed model shows how to fuzzify multiple observations into a fuzzy set. Neural networks are applied for training and then forecasting the consecutive fuzzy sets. The results of the forecasting are defuzzified into forecasts. We use daily observations of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as a forecasting target for the period from 2001 to 2006. The TAIEX is separated into in-sample and out-of-sample observations. The in-sample observations are used for training and the out-of-sample observations for forecasting. The empirical results demonstrate that the proposed model performs better.