Mail Volume Forecasting an Evaluation of Machine Learning Models

Abstract: This study applies machine learning models to mail volumes with the goal of making sufficiently accurate forecasts to minimise the problem of under- and overstaffing at a mail operating company. A most suitable model appraisal in the context is found by evaluating input features and three different models, Auto Regression (AR), Random Forest (RF) and Neural Network (NN) (Multilayer Perceptron (MLP)). The results provide exceedingly improved forecasting accuracies compared to the model that is currently in use. The RF model is recommended as the most practically applicable model for the company, although the RF and NN models provide similar accuracy. This study serves as an initiative since the field lacks previous research in producing mail volume forecasts with machine learning. The outcomes are predicted to be applicable for mail operators all over Sweden and the World.