A Novel Seasonal Fuzzy Time Series Method to the Forecasting of Air Pollution Data in Ankara

Fuzzy time series forecasting methods have been widely studied in recent years. This is because fuzzy time series forecasting methods are compatib le with flexib le calculat ion techniques and they do not require constraints that exist in conventional time series approaches. Most of the real life time series exh ibit periodical changes arising from seasonality. These variations are called seasonal changes. Although, conventional time series approaches for the analysis of time series which have seasonal effect are abundant in literature, the number of fuzzy t ime series approaches is limited. In almost all of these studies, membership values are ignored in the analysis process. This affects forecasting performance of the approach negatively due to the loss of information as well as posing a situation that is incompatible with the basic features of fuzzy set theory. In this study, for the first time in literature, a new seasonal fuzzy time series approach which considers membership values in both identificat ion of fuzzy relat ions and defuzzification steps was proposed. In the proposed method, we used fuzzy C-means clustering method in fuzzification step and artificial neural networks (ANN) in identification of fuzzy relation and defuzzification steps which consider membership values. The proposed method was applied to various seasonal fuzzy time series and obtained results were compared with some conventional and fuzzy time series approaches. In consequence of this evaluation, it was determined that forecasting performance of the proposed method is satisfactory.