During the past decades, forecasting models based on the concept of fuzzy time series have been proposed. There are two main factors, which are the lengths of intervals and the content of forecast rules that will impact the forecasted accuracy of the models. How to find the proper content of the main factors to improve the forecasted accuracy has become an interesting research topic. Some forecasting models, which combined heuristic methods or evolutionary algorithms (such as genetic algorithms and particle swarm optimization) with the fuzzy time series, have been proposed. In this thesis, we use the reduced space searching algorithm (RSSA) to find the proper content of the main factors. A new hybrid forecasting model which combined RSSA with fuzzy time series is proposed to improve the forecasted accuracy. The experimental results of forecasting enrollments of students of the University of Alabama and TAIFEX (Taiwan Stock Index Futures) forecasting problems show that this new model is better than the existing models.

During the past decades, forecasting models based on the concept of fuzzy time series have been proposed. There are two main factors, which are the lengths of intervals and the content of forecast rules that will impact the forecasted accuracy of the models. How to find the proper content of the main factors to improve the forecasted accuracy has become an interesting research topic. Some forecasting models, which combined heuristic methods or evolutionary algorithms (such as genetic algorithms and particle swarm optimization) with the fuzzy time series, have been proposed. In this thesis, we use the reduced space searching algorithm (RSSA) to find the proper content of the main factors. A new hybrid forecasting model which combined RSSA with fuzzy time series is proposed to improve the forecasted accuracy. The experimental results of forecasting enrollments of students of the University of Alabama and TAIFEX (Taiwan Stock Index Futures) forecasting problems show that this new model is better than the existing models.

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