HYBRID SARIMA-AUTOFORMER MODELING FOR RETAIL DEMAND
FORECASTING
Demand forecasting, Time series, Hybrid models, Autoformer, Retail.
Demand forecasting plays a strategic role in retail management, contributing to inventory
optimization, reduction of operational costs, and improvement of the consumer experience.
This study proposes the evaluation of hybrid models for sales forecasting, with an emphasis
on modeling the residuals generated by traditional models. The research begins with a
literature review, which highlights the predominance of architectures based on Long Short-
Term Memory (LSTM) and Random Forest (RF) as hybridization strategies. However, a gap
is identified regarding the application of the Autoformer model in conjunction with Zhang’s
(2003) methodology. The Autoformer, by incorporating time series decomposition
mechanisms and autocorrelation, shows potential to capture seasonal patterns and long-term
trends. The Seasonal AutoRegressive Integrated Moving Average (SARIMA) is adopted as
the base model, and the Autoformer as the hybridization strategy for modeling the residuals,
whose performance will be compared with recurrent approaches in the literature, such as
LSTM and RF. Using sales time series from the Ecuadorian retail chain La Favorita, the study
evaluates the performance of the approaches through performance metrics. The results are
expected to contribute to methodological advancements in univariate and seasonal time series
forecasting, as well as provide practical applications in demand forecasting.