A Stack-based Ensemble Model with Explainability for Food Demand Forecasting

Ahstract- The demand forecasting method involves estimating the number of products that customers will buy using previous data. Many industries, including the food sector and retail, employ this prediction exercise. Prediction is essential in restaurants since the majority of fundamental products have a limited shelf life. Demands are influenced by a variety of overt and covert circumstances, including season, area, and others. In this study, machine learning uses multiple data sources, like internal and external data, to forecast the supply of various goods based on demand. Although various machine learning models have already been applied to predicting demand, very limited work has been performed to explain the black-box nature of the model. In this work, an attempt is made to explain the interpretability of the model. Here, we first present the food demand prediction problem as a regression problem and then apply various machine learning models to predict the demand for food. After that, a stacked-based ensemble model is employed to address various concerns coming from the base models, achieving better prediction. Finally, the interpretability is resolved to utilize effective learning techniques like Local Interpretable Model-agnostic Explanations (LIME).