Probabilistic forecasting of heterogeneous consumer transaction–sales time series

Lindsay R. Berry, Paul Helman and Mike West

International Journal of Forecasting, 2020, vol. 36, issue 2, 552-569

Abstract: We present new Bayesian methodology for consumer sales forecasting. Focusing on the multi-step-ahead forecasting of daily sales of many supermarket items, we adapt dynamic count mixture models for forecasting individual customer transactions, and introduce novel dynamic binary cascade models for predicting counts of items per transaction. These transaction–sales models can incorporate time-varying trends, seasonality, price, promotion, random effects and other outlet-specific predictors for individual items. Sequential Bayesian analysis involves fast, parallel filtering on sets of decoupled items, and is adaptable across items that may exhibit widely-varying characteristics. A multi-scale approach enables information to be shared across items with related patterns over time in order to improve prediction, while maintaining the scalability to many items. A motivating case study in many-item, multi-period, multi-step-ahead supermarket sales forecasting provides examples that demonstrate an improved forecast accuracy on multiple metrics, and illustrates the benefits of full probabilistic models for forecast accuracy evaluation and comparison.

Keywords: Bayesian forecasting; Decouple/recouple; Dynamic binary cascade; Forecast calibration; Intermittent demand; Multi-scale forecasting; Predicting rare events; Sales per transaction; Supermarket sales forecasting (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207019302055
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:2:p:552-569

DOI: 10.1016/j.ijforecast.2019.07.007

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().