'Horses for Courses' in demand forecasting

By Fotios Petropoulos, Spyros Makridakis, Vassilios Assimakopoulos, Konstantinos Nikolopoulos. 2014

210 citations recorded by the source corpus.

Forecasting as a scientific discipline has progressed a lot in the last 40years, with Nobel prizes being awarded for seminal work in the field, most notably to Engle, Granger and Kahneman. Despite these advances, even today we are unable to answer a very simple question, the one that is always the first tabled during discussions with practitioners: “what is the best method for my data?”. In essence, as there are horses for courses, there must also be forecasting methods that are more tailored to some types of data, and, therefore, enable practitioners to make informed method selection when facing new data. The current study attempts to shed light on this direction via identifying the main determinants of forecasting accuracy, through simulations and empirical investigations involving 14 popular forecasting methods (and combinations of them), seven time series features (seasonality, trend, cycle, randomness, number of observations, inter-demand interval and coefficient of variation) and one strategic decision (the forecasting horizon). Our main findings dictate that forecasting accuracy is influenced as follows: (a) for fast-moving data, cycle and randomness have the biggest (negative) effect and the longer the forecasting horizon, the more accuracy decreases; (b) for intermittent data, inter-demand interval has bigger (negative) impact than the coefficient of variation; and (c) for all types of data, increasing the length of a series has a small positive effect.

Subfields: Economics and Econometrics, Management Science and Operations Research

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Citations in the corpus

Forecasting in social settings: The state of the art (2020, background): “…approaches that use knowledge obtained from the data to find temporary solutions to the problems faced (Fildes and Petropoulos, 2015), approaches based on time series features and expected errors (Wang et al., 2009; Petropoulos et al., 2014) and approaches based on expert rules (Adya et al., 2001).”
Tales from tails: On the empirical distributions of forecasting errors and their implication to risk (2019, methodology): “Phillips (1979) was the first to discuss how the statistical dependence between the parameter estimates and the final period observations used to generate forecasts affect the distributions of the forecasting errors, stressing the importance of utilizing robust forecasting methods. Chen (1997) provi”
Exploring the sources of uncertainty: Why does bagging for time series forecasting work? (2018, background): “Moreover, less than 99 bootstraps might be considered; as Petropoulos (2014) suggests, the improvements of bagging for time series seem to taper oﬀ after 50 bootstraps.”