Prediction of the Number of Parcels Sent via Express Mail Service in a Time Series Using Statistical and Machine Learning Models

The prediction of the number of parcels sent via express mail during a working day from a single postal center to multiple destination locations is crucial for optimizing the logistics and operational processes of postal operators. Operators can identify seasonal and daily patterns in the time series of parcel shipments, enabling more efficient decision-making through analysis. Expected benefits include improved planning of human, transport, and storage resources and capacities, as well as enhanced customer experience. This study focuses on the Zenica Postal Center in Bosnia and Herzegovina (BiH), where the time series represents the total number of parcels sent hourly. The designed database encompasses a time series spanning 23 days and 5 hours, with each working day lasting 12 hours, resulting in a total of 281 observations. Traditional statistical models, such as Seasonal AutoRegressive Integrated Moving Average (SARIMA) and Prophet, were developed for time series analysis and prediction, alongside models based on machine learning techniques, including Support Vector Regression (SVR) and Artificial Neural Network (ANN). Each model was tested on 10% of the total dataset observations, and the evaluation and comparison of predictive performance were conducted using Mean Absolute Error (MAE) and Mean Squared Error (MSE) metrics.