VMD-LSTM-BMA: A hybrid model for enhancing fresh food sales forecasting and uncertainty estimation

Abstract

Accurate sales forecasting of fresh food is imperative for retailers. It facilitates maintaining optimal inventory levels, thereby enhancing customer satisfaction, boosting revenue, and minimizing waste. However, sales sequences of fresh food are subject to multiple compounded factors, exhibiting nonlinearity and non-stationarity, posing challenges for prediction. This paper proposes a novel multi-variable hybrid model, VMD-LSTM-BMA, based on variational mode decomposition (VMD), long short-term memory (LSTM) neural networks, and Bayesian model averaging (BMA), for daily fresh food sales forecasting. Utilizing the posterior distribution generated by BMA, we calculate prediction intervals at various confidence levels to quantify the uncertainty of the forecasting outcomes. Employing a daily banana sales dataset from a retail chain supermarket, we validate the predictive performance of the proposed hybrid model at different aggregation levels. The results demonstrate that our VMD-LSTM-BMA framework achieves superior point forecasting accuracy compared to other models. In most instances, the prediction intervals provided by VMD-LSTM-BMA exhibit a higher prediction interval coverage probability (PICP) and a narrower interval width. Our proposed hybrid model operates robustly and efficiently, capable of providing reliable guidance for retailers’ replenishment and ordering processes, thereby mitigating the risks of out-of-stock and excess inventory.