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In the last few decades many methods have become available for forecasting. As always, when alternatives exist, choices need to be made so that an appropriate forecasting method can be selected and used for the specific situation being considered. This paper reports the results of a forecasting competition that provides information to facilitate such choice. Seven experts in each of the 24 methods forecasted up to 1001 series for six up to eighteen time horizons. The results of the competition are presented in this paper whose purpose is to provide empirical evidence about dgerences found to exist among the various extrapolative (time series) methods used in the competition. KEYWORDS Forecasting Time series Evaluation AccuracyComparison Empirical studyForecasting is an essential activity both at the personal and organizational level. Forecasts can be obtained by:(a) purely judgemental approaches; It is important to understand that there is no such thing as the best approach or method as there is no such thing as the best car or best hi-fi system. Cars or hi-fis differ among themselves and are bought by people who have different needs and budgets. What is important, therefore, is not to look for 'winners' or 'losers', but rather to understand how various forecasting approaches and methods differ from each other and how information can be provided so that forecasting users can be able to make rational choices for their situation.Empirical studies play an important role in better understanding the pros and cons of the various forecasting approaches or methods (they can be thought of as comparable to the testsconducted by consumer protection agencies when they measure the characteristics of various products).In forecasting, accuracy is a major, although not the only factor (see note by Carbone in this issue of the Journal of Forecasring) that has been dealt with in the forecasting literature by empirical or experimental studies. Summaries of the results of published empirical studies dealing with accuracy can be found in Armstrong (1978), Makridakis and Hibon (1979), and Slovic (1972). The general conclusions from these three papers are: (a) Judgemental approaches are not necessarily more accurate than objective methods: (b) Causal or explanatory methods are not necessarily more accurate than extrapolative methods: and (c) More complex or statistically sophisticated methods are not necessarily more accurate than simpler methods. The present paper is another empirical study concerned mainly with the post-sample forecasting accuracy of extrapolative (time series) methods. The study was organized as a 'forecasting competition' in which expert participants analysed and forecasted many real life time series.This paper extends and enlarges the study by Makridakis and Hibon (1979). The major differences between the present and the previous study owe their origins to suggestions made during a discussion of the previous study at a meeting of the Royal Statistical Society (see Makridakis and Hibon. 1979) and in privat...