earticle

영어

The postal service sector uses machine learning to forecast delivery time and customer traffic. Studies on postal logistics forecasting have used various machine learning algorithms, but there were no attempts using Seasonal and Trend Decomposition using Loess (STL) decomposition, which is frequently used in other fields of time series forecasting. Therefore, this paper proposes a method of applying optimal STL decomposition cycles using the machine learning models of prior studies and the latest machine learning models. First, the proposed method decomposes the daily traffic using STL decomposition to generate three variables (Trend, Seasonal, and Residual). These variables are added to the existing input data variable to train the machine learning model. Finally, a suitable STL decomposition cycle for the model is selected to derive an optimal model. The proposed method was validated by creating nine machine learning (AdaBoost Regression, Random Forest Regression, Ridge, etc.) and two deep learning (DNN, LSTM) models and testing them. As a result, the application of STL decomposition reduced the forecast errors in all models except LSTM. In terms of the proposed method, linear regression had the lowest forecast error, and LSTM had the highest.