论文信息 - Model Identification in Exponential Smoothing

Model Identification in Exponential Smoothing

Model identification has traditionally been ignored in forecasting via exponential smoothing. The usual practice is to apply the same model to every time-series in a collection. This paper develops a procedure for model identification in large forecasting applications based on an examination of variances of differences of the time-series. The order of differencing yielding minimum variance suggests an appropriate model for the series. Empirical results show that this procedure selects models that give reasonable ex ante forecast accuracy.

Everette S. Gardner | Ed. McKenzie | E. S. Gardner | E. McKenzie

[1] M. J. R. Healy,et al. Smoothing, Forecasting and Prediction of Discrete Time Series , 1964 .

[2] Gwilym M. Jenkins,et al. Time series analysis, forecasting and control , 1972 .

[3] C. Granger,et al. Experience with Forecasting Univariate Time Series and the Combination of Forecasts , 1974 .

[4] Douglas C. Montgomery,et al. Forecasting and time series analysis , 1976 .

[5] George E. P. Box,et al. Time Series Analysis: Forecasting and Control , 1977 .

[6] Robert L. Winkler,et al. The accuracy of extrapolation (time series) methods: Results of a forecasting competition , 1982 .

[7] Emanuel Parzen,et al. ARARMA models for time series analysis and forecasting , 1982 .

[8] E. McKenzie. General exponential smoothing and the equivalent arma process , 1984 .

[9] Everette S. Gardner,et al. Exponential smoothing: The state of the art , 1985 .

[10] E. S. Gardner,et al. Forecasting Trends in Time Series , 1985 .

[11] E. S. Gardner,et al. Seasonal exponential smoothing with damped trends , 1989 .