Demand Forecasting for Daily Retail Orders in Fresh Food Market

In the complex landscape of fresh food retail, accurate demand forecasting stands as a critical challenge, pivotal to both financial optimization and sustainable practices. This paper introduces an advanced approach to daily retail order predictions by applying multiple machine learning paradigms: Long Short-Term Memory (LSTM) networks, Transformers, Prophet, Feedforward Neural Networks (FNN), and eXtreme Gradient Boosting (XGBoost). Leveraging LSTM’s adeptness at processing sequential sales data, we harness its capability to discern temporal patterns and long-term dependencies inherent in the sales trajectory. Transformers can handle longer dependencies, which have been shown to be beneficial for our yearly seasonal data. On the other hand, XGBoost, with its robustness against overfitting and ability to capture nonlinear relationships, provides a granular understanding of the domain-related factors. FNNs are included as a strong baseline, and the included residual connections have proven crucial due to our sales data’s correlation with the next day’s sales being the most pronounced. Through experimentations with a dataset of sales data from the past seven years, our research highlights the strength of these methodologies. The results reveal that all models perform in acceptable ranges, however, Transformers, XGBoost and FNN models outperform LSTMs. Additionally, XGBoost exhibits slightly lower variation, which may be preferable.