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Forecasting from time series subject to sporadic perturbations: Effectiveness of different types of forecasting support

De Baets, S; Harvey, N; (2018) Forecasting from time series subject to sporadic perturbations: Effectiveness of different types of forecasting support. International Journal of Forecasting , 34 (2) pp. 163-180. 10.1016/j.ijforecast.2017.09.007. Green open access

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Abstract

How effective are different approaches for the provision of forecasting support? Forecasts may be either unaided or made with the help of statistical forecasts. In practice, the latter are often crude forecasts that do not take sporadic perturbations into account. Most research considers forecasts based on series that have been cleansed of perturbation effects. This paper considers an experiment in which people made forecasts from time series that were disturbed by promotions. In all conditions, under-forecasting occurred during promotional periods and over-forecasting during normal ones. The relative sizes of these effects depended on the proportions of periods in the data series that contained promotions. The statistical forecasts improved the forecasting accuracy, not because they reduced these biases, but because they decreased the random error (scatter). The performance improvement did not depend on whether the forecasts were based on cleansed series. Thus, the effort invested in producing cleansed time series from which to forecast may not be warranted: companies may benefit from giving their forecasters even crude statistical forecasts. In a second experiment, forecasters received optimal statistical forecasts that took the effects of promotions into account fully. This increased the accuracy because the biases were almost eliminated and the random error was reduced by 20%. Thus, the additional effort required to produce forecasts that take promotional effects into account is worthwhile.

Type: Article
Title: Forecasting from time series subject to sporadic perturbations: Effectiveness of different types of forecasting support
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.ijforecast.2017.09.007
Publisher version: http://dx.doi.org/10.1016/j.ijforecast.2017.09.007
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Social Sciences, Economics, Management, Business & Economics, Forecasting support, Judgmental adjustment, Time series, Promotions, Sales, PROBABILISTIC FORECASTS, JUDGMENTAL ADJUSTMENTS, MANAGEMENT JUDGMENT, STOCK-PRICES, INFORMATION, PERFORMANCE, INTEGRATION, HEURISTICS, ACCURACY, ILLUSION
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Experimental Psychology
URI: https://discovery.ucl.ac.uk/id/eprint/10054985
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