Fuzzy Time Series Forecasting with Different Interval Selection and a Case Study for Adana Dam Occupancy Rate Forecasting

Abstract

In the literature, various methods are suggested and various studies are conducted to improve existing methods to obtain the best forecasting. The purpose of this study is to show that although fuzzy time series has the disadvantage of interval selection, it can give as good results as other popular methods and emphasize that it is a more dynamic method. In this study, the monthly dam occupancy rates of the Adana/Turkey province were used as the dataset, and forecasts were made using autoregressive integrated moving average, decomposition, and fuzzy time series methods. The effect of different interval selections on the forecasting performance in fuzzy time series is also investigated. This study shows that choosing the correct interval significantly impacts the performance of fuzzy time series. This study shows that choosing the correct interval significantly impacts the performance of fuzzy time series. The prediction performance of the fuzzy time series model for extreme values was much better than other prediction methods used in the study. According to the results and evaluations, the fuzzy time series method produces dynamic and robust forecasts close to reality and gives good results with the right interval number and length selection.

Keywords

• fuzzy time series
• interval selection
• ARIMA
• decomposition
• dam occupancy rate

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