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A fuzzy seasonal ARIMA model for forecasting. (English) Zbl 1003.62081

Summary: 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 is 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 values. 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.

MSC:

62M20 Inference from stochastic processes and prediction
62P30 Applications of statistics in engineering and industry; control charts
62M99 Inference from stochastic processes
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