Abstract

In this study, the authors used 111 time series to examine the accuracy of various forecasting methods, particularly time-series methods. The study shows, at least for time series, why some methods achieve greater accuracy than others for different types of data. The authors offer some explanation of the seemingly conflicting conclusions of past empirical research on the accuracy of forecasting. One novel contribution of the paper is the development of regression equations expressing accuracy as a function of factors such as randomness, seasonality, trend-cycle and the number of data points describing the series. Surprisingly, the study shows that for these 111 series simpler methods perform well in comparison to the more complex and statistically sophisticated ARMA models.

Accuracy of Forecasting: An Empirical Investigation, Spyros Makridakis, Michele Hibon and Claus Moser, Journal of the Royal Statistical Society. Series A (General), Vol. 142, No. 2 (1979), pp. 97-145.

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