Generalising about univariate forecasting methods: Further empirical evidence

Abstract This paper extends the empirical evidence on the forecasting accuracy of extrapolative methods. The robustness of the major conclusions of the M-Competition data is examined in the context of the telecommunications data of Fildes (1992) . The performance of Robust Trend, found to be a successful method for forecasting the telecommunications data by Fildes, is compared with that of other successful methods using the M-Competition data. Although it is established that the structure of the telecommunications data is more homogeneous than that of the M-Competition data, the major conclusions of the M-Competition continue to hold for this new data set. In addition, while the Robust Trend method is confirmed to be the best performing method for the telecommunications data, for the 1001 M-Competition series, this method is outperformed by methods such as Single or Damped Smoothing. The performance of smoothing methods depended on how the smoothing parameters are estimated. Optimisation at each time origin was more accurate than optimisation at the first time origin, which in turn is shown to be superior to arbitrary (literature based) fixed values. In contrast to the last point, a data based choice of fixed smoothing constants from a cross-sectional study of the time series was found to perform well.

[1]  Dennis A. Ahlburg,et al.  Error measures and the choice of a forecast method , 1992 .

[2]  Werner A. Stahel,et al.  Forecasting demand for special telephone services: A case study , 1990 .

[3]  Robert L. Winkler,et al.  The Forecasting accuracy of major time series methods , 1986 .

[4]  J. Ledolter.  The effect of additive outliers on the forecasts from ARIMA models , 1989 .

[5]  Michael D. Geurts,et al.  Forecasting retail sales using alternative models , 1986 .

[6]  R. Fildes,et al.  The Impact of Empirical Accuracy Studies On Time Series Analysis and Forecasting , 1995 .

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

[8]  Chris Chatfield,et al.  A commentary on error measures , 1992 .

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

[10]  R. Clemen.  Combining forecasts: A review and annotated bibliography , 1989 .

[11]  G. A. Coutie,et al.  Short-term forecasting , 1965 .

[12]  Muhittin Oral,et al.  A mathematical programming model for market share prediction , 1989 .

[13]  Robert Fildes,et al.  The accuracy of extrapolation methods; an automatic box–jenkins package sift , 1984 .

[14]  LeRoy F. Simmons.  M-competition — A closer look at NAIVE2 and median APE: A note , 1986 .

[15]  Gaetan Libert,et al.  The M‐competition with a fully automatic box–jenkins procedure , 1984 .

[16]  N. Ravishanker,et al.  Reallocation Outliers in Time Series , 1993 .

[17]  Spyros Makridakis,et al.  The art and science of forecasting An assessment and future directions , 1986 .

[18]  R. L. Winkler,et al.  Averages of Forecasts: Some Empirical Results , 1983 .

[19]  Robert Fildes,et al.  The evaluation of extrapolative forecasting methods , 1992 .

[20]  Leonard J. Tashman,et al.  The use of protocols to select exponential smoothing procedures: A reconsideration of forecasting competitions , 1996 .

[21]  Nigel Meade,et al.  ARARMA vs ARIMA—A study of the benefits of a new approach to forecasting , 1985 .

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

[23]  C. Chatfield,et al.  The M2-competition: A real-time judgmentally based forecasting study , 1993 .

[24]  Chris Chatfield,et al.  The Holt-Winters Forecasting Procedure , 1978 .

[25]  Fred L. Collopy,et al.  Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons , 1992 .

[26]  Anne B. Koehler,et al.  A comparison of results from state space forecasting with forecasts from the Makridakis Competition , 1988 .