The choice of a forecasting model

The major purpose of studies of forecasting accuracy is to help forecasters select the 'best' forecasting method. This paper examines accuracy studies in particular that of Makridakis et al. [20] with a view to establishing how they contribute to model choice. It is concluded that they affect the screening that most forecasters go through in selecting a range of methods to analyze--in Bayesian terms they are a major determinant of 'prior knowledge'. This general conclusion is illustrated in the specific case of the Makridakis Competition (M-Competition). A survey of expert forecasters was made in both the UK and US. The respondents were asked about their familiarity with eight methods of univariate time series forecasting, and their perceived accuracy in three different forecasting situations. The results, similar for both the UK and US, were that the forecasters were relatively familiar with all the techniques included except Holt-Winters and Bayesian. For short horizons Box-Jenkins was seen as most accurate while trend curves was perceived as most suitable for the long horizons. These results are contrasted with those of the M-Competition, and conclusions drawn on how the results of the M-Competition should influence model screening and model choice.

[1]  J. Scott Armstrong,et al.  Commentary on the Makridakis Time Series Competition (M-Competition) , 2005 .

[2]  Gwilym M. Jenkins,et al.  Some practical aspects of forecasting in organizations , 1982 .

[3]  Mark B. Schupack.  The Predictive Accuracy of Empirical Demand Analyses , 1962 .

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

[5]  John T. Mentzer,et al.  Familiarity, application, and performance of sales forecasting techniques , 1984 .

[6]  N. Meade.  The use of growth curves in forecasting market development—a review and appraisal , 1984 .

[7]  C. F. Stevens,et al.  A Bayesian Approach to Short-term Forecasting , 1971 .

[8]  Douglas J. Dalrymple.  Sales forecasting methods and accuracy , 1975 .

[9]  W. H. Williams,et al.  A Simple Method for the Construction of Empirical Confidence Limits for Economic Forecasts , 1971 .

[10]  Robert Carbone,et al.  Comparing for Different Time Series Methods the Value of Technical Expertise Individualized Analysis, and Judgmental Adjustment , 1983 .

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

[12]  J. Sparkes,et al.  Awareness and use of forecasting techniques in british industry , 1984 .

[13]  A. Harvey.  Time series models , 1983 .

[14]  R. L. Winkler,et al.  The Combination of Forecasts , 1983 .

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

[16]  Robert Fildes,et al.  Quantitative Forecasting — The State of the Art: Extrapolative Models , 1979 .

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

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

[19]  C. D. Beaumont,et al.  A Bibliography of Business and Economic Forecasting , 1981 .

[20]  Paul Newbold,et al.  Some comments on the evaluation of economic forecasts , 1973 .

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

[22]  Steven C. Wheelwright,et al.  Corporate Forecasting: Promise and Reality. , 1976 .

[23]  R. Lewandowski.  Practitioners' forum. Sales forecasting by forsys , 1982 .

[24]  P. Wason.  On the Failure to Eliminate Hypotheses in a Conceptual Task , 1960 .