Forecasting seasonally fluctuating sales of perishable products in the horticultural industry

Josef Eiglsperger, Florian Haselbeck, Viola Stiele, Claudia Guadarrama Serrano, Kelly Lim-Trinh, Klaus Menrad, Thomas Hannus, Dominik G. Grimm

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

3 Zitate (Scopus)

Abstract

Accurately forecasting demand is a potential competitive advantage, especially when dealing with perishable products. The multi-billion dollar horticultural industry is highly affected by perishability, but has received limited attention in forecasting research. In this paper, we analyze the applicability of general compared to dataset-specific predictors, as well as the influence of external information and online model update schemes. We employ a heterogeneous set of horticultural data, three classical, and twelve machine learning-based forecasting approaches. Our results show a superiority of multivariate machine learning methods, in particular the ensemble learner XGBoost. These advantages highlight the importance of external factors, with the feature set containing statistical, calendrical, and weather-related features leading to the most robust performance. We further observe that a general model is unable to capture the heterogeneity of the data and is outperformed by dataset-specific predictors. Moreover, frequent model updates have a negligible impact on forecasting quality, allowing long-term forecasting without significant performance degradation.

OriginalspracheEnglisch
Aufsatznummer123438
FachzeitschriftExpert Systems with Applications
Jahrgang249
DOIs
PublikationsstatusVeröffentlicht - 1 Sept. 2024

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