Forecasting seasonal time series based on fuzzy techniques

This paper is devoted to a method for the forecasting of seasonal time series. The core of our approach is based on the fuzzy transform and fuzzy natural logic (FNL) techniques. Under the assumption that a time series can be additively decomposed into a trend-cycle, a seasonal component and an irregular fluctuation, the forecasting is a combination of individual forecasting of each of these constituents. More precisely, the trend-cycle and the seasonal component are predicted with the help of fuzzy transform, pattern recognition and fuzzy natural logic techniques. To model the irregular component, we apply the Box-Jenkins approach. In the paper, we compare the suggested method with two other well-known methods, namely STL (see [14] ) and ARIMA ones. (C) 2018 Elsevier B.V. All rights reserved.