| 1. |
- Grossmann, Igor, et al.
(författare)
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Insights into the accuracy of social scientists' forecasts of societal change
- 2023
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Ingår i: Nature Human Behaviour. - : Springer Nature. - 2397-3374. ; 7, s. 484-501
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Tidskriftsartikel (refereegranskat)abstract
- How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists' forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data. How accurate are social scientists in predicting societal change, and what processes underlie their predictions? Grossmann et al. report the findings of two forecasting tournaments. Social scientists' forecasts were on average no more accurate than those of simple statistical models.
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| 2. |
- Petropoulos, Fotios, et al.
(författare)
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Another look at estimators for intermittent demand
- 2016
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Ingår i: International Journal of Production Economics. - : Elsevier. - 0925-5273 .- 1873-7579. ; 181, s. 154-161
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Tidskriftsartikel (refereegranskat)abstract
- In this paper we focus on forecasting for intermittent demand data. We propose a new aggregation framework for intermittent demand forecasting that performs aggregation over the demand volumes, in contrast to the standard framework that employs temporal (over time) aggregation. To achieve this we construct a transformed time series, the inverse intermittent demand series. The new algorithm is expected to work best on erratic and lumpy demand, as a result of the variance reduction of the non-zero demands. The improvement in forecasting performance is empirically demonstrated through an extensive evaluation in more than 8000 time series of two well-researched spare parts data sets from the automotive and defence sectors. Furthermore, a simulation is performed so as to provide a stock-control evaluation. The proposed framework could find popularity among practitioners given its suitability when dealing with clump sizes. As such it could be used in conjunction with existing popular forecasting methods for intermittent demand as an exception handling mechanism when certain types of demand are observed.
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| 3. |
- Petropoulos, Fotios, et al.
(författare)
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Judgmental selection of forecasting models
- 2018
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Ingår i: Journal of Operations Management. - : John Wiley & Sons. - 0272-6963 .- 1873-1317. ; 60, s. 34-46
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we explored how judgment can be used to improve the selection of a forecasting model. We compared the performance of judgmental model selection against a standard algorithm based on information criteria. We also examined the efficacy of a judgmental model-build approach, in which experts were asked to decide on the existence of the structural components (trend and seasonality) of the time series instead of directly selecting a model from a choice set. Our behavioral study used data from almost 700 participants, including forecasting practitioners. The results from our experiment suggest that selecting models judgmentally results in performance that is on par, if not better, to that of algorithmic selection. Further, judgmental model selection helps to avoid the worst models more frequently compared to algorithmic selection. Finally, a simple combination of the statistical and judgmental selections and judgmental aggregation significantly outperform both statistical and judgmental selections.
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| 4. |
- Petropoulos, Fotios, et al.
(författare)
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Judgmental selection of forecasting models (reprint)
- 2023
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Ingår i: Judgment in Predictive Analytics. - Cham : Springer. - 9783031300844 - 9783031300875 - 9783031300851 ; , s. 53-84
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Bokkapitel (refereegranskat)abstract
- In this paper, we explored how judgment can be used to improve the selection of a forecasting model. We compared the performance of judgmental model selection against a standard algorithm based on information criteria. We also examined the efficacy of a judgmental model-build approach, in which experts were asked to decide on the existence of the structural components (trend and seasonality) of the time series instead of directly selecting a model from a choice set. Our behavioral study used data from almost 700 participants, including forecasting practitioners. The results from our experiment suggest that selecting models judgmentally results in performance that is on par, if not better, to that of algorithmic selection. Further, judgmental model selection helps to avoid the worst models more frequently compared to algorithmic selection. Finally, a simple combination of the statistical and judgmental selections and judgmental aggregation significantly outperform both statistical and judgmental selections.
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