Forecasting of returns quantities in parcel logistics

The selection of the processes is based on the state-of-the-art. In cooperation with the customer, historical data is collected and the benchmark is defined. For this purpose, the time periods for training and test data as well as suitable error measures are agreed upon. Using Jupyter notebooks, the data is first analyzed in terms of a classic time series analysis. Concepts such as stationarity, trend and seasonality of the time series are examined in order to optimally parameterize different forecasting methods such as (S)ARIMA, (T)BATs, Exponential Smoothing, Holt-Winters, Random Forest and XGBoost. The development and evaluation of the prediction models is done using well-known Python libraries such as fbprophet, matplotlib, pandas, scikit-learn, scipy, statsmodels and tbats.