A weighted Fuzzy time series model for forecasting seasonal data(Suatu model siri masakabur berpemberat untuk meramal data bermusim)

Abstract

This study proposes a weighted model and graphical order selection in fuzzy seasonal time series forecasting. Initially, the fuzzy relationships were treated as if they were equally important, which might not properly reflected the importance of each individual fuzzy relationship in forecasting. Then, a linear chronological weight is introduced to handle the importance of each chronological individual fuzzy relationship. This paper proposes a naïve, uniform, and exponential chronological weight which is developed based on the concept of naïve, moving average, and exponential smoothing methods. In addition, graphical order fuzzy relationship is proposed to identify the best Fuzzy Logical Relationship order of fuzzy time series model. A quarterly data set is selected to illustrate the proposed method and to compare the forecasting accuracy with three other fuzzy time series models and two classical time series models. The results of the comparison using the test data show that the proposed method produces more precise forecast values than the other methods.