Modeling Holiday Effect on Retail Demand Forecasting: A Systematic Review

Holidays generate sharp deviations in retail demand, creating surges before major events and steep declines afterward, and often account for a disproportionately large share of annual sales. Accurately forecasting such fluctuations is essential for inventory planning, pricing, and supply chain coordination, yet presents unique challenges. This review synthesizes the literature on modeling holiday impacts in retail sales forecasting, emphasizing how holiday-related features are designed, integrated, and evaluated. We identify five central themes: (1) the decision on whether to model the effect of a certain holiday; (2) the inter-temporal dynamics of holiday effects, including pre-holiday stock-ups and post-holiday decay; (3) cross-sectional heterogeneity across products and categories; (4) clustering and categorization strategies for grouping holidays; and (5) the integration of holiday and non-holiday data through unified or separate modeling frameworks. For each theme, we summarize the contribution of current literature and propose possible future research directions. We also proposed a unified decision flow for modeling holiday impact in demand forecasting. This paper provides both a roadmap for researchers seeking to advance methodological rigor and a guide for practitioners aiming to improve operational forecasting accuracy during critical holiday periods.