Tine Van Calster, Filip Van den Bossche, Bart Baesens, Wilfried Lemahieu
arXiv 3 Feb 2020 · Econometrics · 4 citations (OpenAlex)
arXiv:2002.00949 · PDF · DOI · OpenAlex · Extracted main text
Choosing the technique that is the best at forecasting your data, is a problem that arises in any forecasting application. Decades of research have resulted into an enormous amount of forecasting methods that stem from statistics, econometrics and machine learning (ML), which leads to a very difficult and elaborate choice to make in any forecasting exercise. This paper aims to facilitate this process for high-level tactical sales forecasts by comparing a large array of techniques for 35 times series that consist of both industry data from the Coca-Cola Company and publicly available datasets. However, instead of solely focusing on the accuracy of the resulting forecasts, this paper introduces a novel and completely automated profit-driven approach that takes into account the expected profit that a technique can create during both the model building and evaluation process. The expected profit function that is used for this purpose, is easy to understand and adaptable to any situation by combining forecasting accuracy with business expertise. Furthermore, we examine the added value of ML techniques, the inclusion of external factors and the use of seasonal models in order to ascertain which type of model works best in tactical sales forecasting. Our findings show that simple seasonal time series models consistently outperform other methodologies and that the profit-driven approach can lead to selecting a different forecasting model.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Crone, S. F., Hibon, M., Nikolopoulos, K (2011) Advances in forecasting with neural networks? empirical evidence from the nn3 competition on time series prediction | 1.000 | 10 | 4 | 100% |
| 2 | Makridakis, S., Hibon, M (2000) The m3-competition: results, conclusions and implications | 1.000 | 6 | 4 | 100% |
| 3 | Athanasopoulos, G., Hyndman, R. J., Song, H., Wu, D. C (2011) The tourism forecasting competition | 1.000 | 6 | 3 | 100% |
| 4 | Cang, S., Yu, H (2014) A combination selection algorithm on forecasting | 0.874 | 7 | 2 | 100% |
| 5 | Lessmann, S., Baesens, B., Seow, H.-V., Thomas, L. C (2015) Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research self | 0.874 | 6 | 2 | 100% |
| 6 | Petropoulos, F., Makridakis, S., Assimakopoulos, V., Nikolopoulos, K (2014) ‘horses for courses’ in demand forecasting | 0.874 | 6 | 2 | 100% |
| 7 | Ma, S., Fildes, R., Huang, T (2016) Demand forecasting with high dimensional data: The case of sku retail sales forecasting with intra-and inter-category promotiona… | 0.811 | 4 | 2 | 100% |
| 8 | Van Calster, T., Baesens, B., Lemahieu, W (2017) Profarima: A profit-driven order identification algorithm for arima models in sales forecasting self | 0.737 | 3 | 2 | 100% |
| 9 | Bozos, K., Nikolopoulos, K (2011) Forecasting the value effect of seasoned equity offering announcements | 0.693 | 6 | 1 | 100% |
| 10 | Akn, M (2015) A novel approach to model selection in tourism demand modeling | 0.644 | 4 | 1 | 100% |
Showing the top 10 of 52 scored citations.