The M2-competition: A real-time judgmentally based forecasting study

Makridakis, Spyros; Chatfield, Chris; Hibon, Michèle; Lawrence, Michael; Mills, Terence C.; Ord, Keith; Simmons, Le Roy F.

doi:10.1016/0169-2070(93)90044-n

Cited by 228 publications

(129 citation statements)

References 14 publications

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“…Makridakis et al, 1993;Makridakis and Hibon, 2000). If the extra parameter is permitted to be greater than one, the method can also produce an exponential forecast function, which would seem to be useful for series with exponential trend.…”

Section: Introductionmentioning

confidence: 99%

Exponential smoothing with a damped multiplicative trend

Taylor

2003

International Journal of Forecasting

234

114

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Multiplicative trend exponential smoothing has received very little attention in the literature.It involves modelling the local slope by smoothing successive ratios of the local level, and this leads to a forecast function that is the product of level and growth rate. By contrast, the popular Holt method uses an additive trend formulation. It has been argued that more real series have multiplicative trends than additive. However, even if this is true, it seems likely that the more conservative forecast function of the Holt method will be more robust when applied in an automated way to a large batch of series with different types of trend. In view of the improvements in accuracy seen in dampening the Holt method, in this paper, we investigate a new damped multiplicative trend approach. An empirical study, using the monthly time series from the M3-Competition, gave encouraging results for the new approach at a range of forecast horizons, when compared to the established exponential smoothing methods.

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Section: Introductionmentioning

confidence: 99%

Exponential smoothing with a damped multiplicative trend

Taylor

2003

International Journal of Forecasting

234

114

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“…However, as described in [26], ''some forecasting methods are very simple and surprisingly effective''. Additionally it has previously been noted that in the realtime forecasting M2-competition [42] that ARIMA (Box-Jenkins) ''proved to be one of the least-accurate methods and its overall median error is 17 % greater than that for a naive forecast'' [3]. Therefore it can be seen that it is not uncommon for naive methods to perform very well.…”

Section: Discussionmentioning

confidence: 95%

Recurrent Cartesian Genetic Programming of Artificial Neural Networks

Turner

Miller

2016

Genet Program Evolvable Mach

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“…Smith's (1997) review led him to conclude that complexity did not produce more accurate population forecasts. Finally, and of key importance, the M-competition studies have shown that simplicity is a virtue in extrapolation (Makridakis et al 1982;Makridakis et al 1993;Makridakis and Hibon 2000).…”

Section: Trend Extrapolationmentioning

confidence: 99%

Extrapolation for Time-Series and Cross-Sectional Data

Armstrong

2001

International Series in Operations Research &Amp; Management Science

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Extrapolation methods are reliable, objective, inexpensive, quick, and easily automated. As a result, they are widely used, especially for inventory and production forecasts, for operational planning for up to two years ahead, and for long-term forecasts in some situations, such as population forecasting. This paper provides principles for selecting and preparing data, making seasonal adjustments, extrapolating, assessing uncertainty, and identifying when to use extrapolation. The principles are based on received wisdom (i.e., experts' commonly held opinions) and on empirical studies. Some of the more important principles are:• In selecting and preparing data, use all relevant data and adjust the data for important events that occurred in the past.• Make seasonal adjustments only when seasonal effects are expected and only if there is good evidence by which to measure them.• In extrapolating, use simple functional forms. Weight the most recent data heavily if there are small measurement errors, stable series, and short forecast horizons. Domain knowledge and forecasting expertise can help to select effective extrapolation procedures. When there is uncertainty, be conservative in forecasting trends. Update extrapolation models as new data are received.• To assess uncertainty, make empirical estimates to establish prediction intervals.• Use pure extrapolation when many forecasts are required, little is known about the situation, the situation is stable, and expert forecasts might be biased. Disciplines Business | Marketing ABSTRACTExtrapolation methods are reliable, objective, inexpensive, quick, and easily automated. As a result, they are widely used, especially for inventory and production forecasts, for operational planning for up to two years ahead, and for long-term forecasts in some situations, such as population forecasting. This paper provides principles for selecting and preparing data, making seasonal adjustments, extrapolating, assessing uncertainty, and identifying when to use extrapolation. The principles are based on received wisdom (i.e., experts' commonly held opinions) and on empirical studies. Some of the more important principles are:

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The M2-competition: A real-time judgmentally based forecasting study

Cited by 228 publications

References 14 publications

Exponential smoothing with a damped multiplicative trend

Exponential smoothing with a damped multiplicative trend

Recurrent Cartesian Genetic Programming of Artificial Neural Networks

Extrapolation for Time-Series and Cross-Sectional Data

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