A new fuzzy time series model based on robust clustering for forecasting of air pollution

Abstract

In this study, a new Fuzzy Time Series (FTS) model based on the Fuzzy K-Medoid (FKM) clustering algorithm is proposed in order to forecast air pollution. FTS models generally have some advantages when compared with other techniques used in forecasting of air pollution as they do not require any statistical assumptions on time series data; and they provide successful forecasting results even in situations where the number of observations is small and where data sets include uncertainty, still allowing for generalization. But existing FTS models based on fuzzy clustering fail in modeling of data sets that include outliers such as air pollution data. The potential superiority of the proposed model is to be a robust technique for outliers and abnormal observations. In order to show the performance of the proposed method in forecasting of air pollution, a time series consisting of SO2 concentrations measured in 65 monitoring stations in Turkey are used. According to the results of analyses, it is observed that the proposed method provides successful forecasting results especially in time series which include numerous outliers.