An ensemble forecasting model for predicting contribution of food donors based on supply behavior

Food banks are nonprofit hunger relief organizations that collect donations from donors and distribute food to local agencies that serve people in need. Donors consist of local supermarkets, manufacturers, and community organizations. The frequency, quantity, and type of food donated by each donor can vary each month. In this research, we propose a technique to identify the supply behavior of donors and cluster them based on these attributes. We then develop a predictive ensemble model to forecast the contribution of different donor clusters. Our study shows the necessary behavioral attributes to classify donors and the best way to cluster donor data to improve the prediction model.

Automated summary

This research proposes a technique to identify the supply behavior of donors and cluster them based on these attributes, as well as developing a predictive ensemble model to forecast the contribution of different donor clusters. The study demonstrates the essential behavioral attributes needed to classify donors and the optimal way to cluster donor data to enhance the prediction model.