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- Ramos, PatríciaCentre for Enterprise Systems Engineering, INESC TEC, Porto Accounting and Business School, Polytechnic of Porto, Asprela, Portugal (author)
Forecasting Seasonal Sales with Many Drivers : Shrinkage or Dimensionality Reduction?
- Article/chapterEnglish2023
Publisher, publication year, extent ...
- 2022-12-26
- MDPI,2023
- electronicrdacarrier
Numbers
- LIBRIS-ID:oai:DiVA.org:his-22318
- https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-22318URI
- https://doi.org/10.3390/asi6010003DOI
Supplementary language notes
- Language:English
- Summary in:English
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- SwePubSwePub
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Notes
- CC BY 4.0© 2022 by the authors.This research received no external funding.
- Retailers depend on accurate forecasts of product sales at the Store × SKU level to efficiently manage their inventory. Consequently, there has been increasing interest in identifying more advanced statistical techniques that lead to accuracy improvements. However, the inclusion of multiple drivers affecting demand into commonly used ARIMA and ETS models is not straightforward, particularly when many explanatory variables are available. Moreover, regularization regression models that shrink the model’s parameters allow for the inclusion of a lot of relevant information but do not intrinsically handle the dynamics of the demand. These problems have not been addressed by previous studies. Nevertheless, multiple simultaneous effects interacting are common in retailing. To be successful, any approach needs to be automatic, robust and efficiently scaleable. In this study, we design novel approaches to forecast retailer product sales taking into account the main drivers which affect SKU demand at store level. To address the variable selection challenge, the use of dimensionality reduction via principal components analysis (PCA) and shrinkage estimators was investigated. The empirical results, using a case study of supermarket sales in Portugal, show that both PCA and shrinkage are useful and result in gains in forecast accuracy in the order of 10% over benchmarks while offering insights on the impact of promotions. Focusing on the promotional periods, PCA-based models perform strongly, while shrinkage estimators over-shrink. For the non-promotional periods, shrinkage estimators significantly outperform the alternatives.
Subject headings and genre
- SAMHÄLLSVETENSKAP Ekonomi och näringsliv Företagsekonomi hsv//swe
- SOCIAL SCIENCES Economics and Business Business Administration hsv//eng
- forecasting
- principal components analysis
- promotions
- retailing
- seasonality
- shrinkage
- Skövde Artificial Intelligence Lab (SAIL)
- Skövde Artificial Intelligence Lab (SAIL)
Added entries (persons, corporate bodies, meetings, titles ...)
- Oliveira, José ManuelCentre for Telecommunications and Multimedia, INESC TEC, Faculty of Economics, University of Porto, Porto, Portugal (author)
- Kourentzes, NikolaosHögskolan i Skövde,Institutionen för informationsteknologi,Forskningsmiljön Informationsteknologi,Skövde Artificial Intelligence Lab (SAIL)(Swepub:his)koun (author)
- Fildes, RobertCentre for Marketing Analytics and Forecasting, Department of Management Science, Lancaster University Management School, United Kingdom (author)
- Centre for Enterprise Systems Engineering, INESC TEC, Porto Accounting and Business School, Polytechnic of Porto, Asprela, PortugalCentre for Telecommunications and Multimedia, INESC TEC, Faculty of Economics, University of Porto, Porto, Portugal (creator_code:org_t)
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- In:Applied System Innovation: MDPI6:12571-5577
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