Identifying Outliers in Fuzzy Time Series

Abstract

Time series analysis is often associated with the discovery of patterns and prediction of features. Forecasting accuracy can be improved by removing identified outliers in the data set using the Cook’s distance and Studentized residual test. In this paper a modified fuzzy time series method is proposed based on transition probability vector membership function. It is experimentally shown that the proposed method minimizes the average forecasting error compared with other known existing methods.