A fuzzy seasonal ARIMA model for forecasting

Abstract

This paper proposes a fuzzy seasonal ARIMA (FSARIMA) forecasting model, which combines the advantages of the seasonal time series ARIMA (SARIMA) model and the fuzzy regression model. It is used to forecast two seasonal time series data of the total production value of the Taiwan machinery industry and the soft drink time series. The intention of this paper is,to provide business which are affected by diversified management with a new method to conduct short-term forecasting. This model includes both interval models with interval parameters and the possible distribution of future value. Based on the results of practical application, it can be shown that this model makes good forecasts and is realistic. Furthermore, this model makes it possible for decision makers to forecast the best and worst estimates based on fewer observations than the SARIMA model. (C) 2002 Elsevier Science B.V. All rights reserved.