Forecasting seasonally fluctuating sales of perishable products in the horticultural industry

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.

Publikationsart
Zeitschriftenbeiträge (peer-reviewed)
Titel
Forecasting seasonally fluctuating sales of perishable products in the horticultural industry
Medien
Expert Systems with Applications
Band
2024
Autoren
Josef Eiglsperger, Florian Haselbeck , Viola Stiele , Claudia Guadarrama Serrano, Kelly Lim-Trinh, Prof. Dr. Klaus Menrad , Prof. Dr. Thomas Hannus , Prof. Dr. Dominik Grimm
Veröffentlichungsdatum
09.02.2024
Zitation
Eiglsperger, Josef; Haselbeck, Florian; Stiele, Viola; Guadarrama Serrano, Claudia; Lim-Trinh, Kelly; Menrad, Klaus; Hannus, Thomas; Grimm, Dominik (2024): Forecasting seasonally fluctuating sales of perishable products in the horticultural industry. Expert Systems with Applications 2024. DOI: 10.1016/j.eswa.2024.123438