## 1 Introduction  
With the rapid development of e-commerce, the demand for delivery services is growing fast. Especially in terms of long-term traditional holidays, including the Lunar New Year and Korean Thanksgiving Day in South Korea, e-commerce gets to its peak, and the delivery service rises dramatically. During these peak periods, logistic organizations have difficulty maintaining normal operations. The peak period is determined to be from the Monday two weeks before the start of a holiday, until two working days after the last day of the holiday period, and the average of the peak periods is 21 days. Demand prediction of parcel delivery service in special periods is critical in developing an optimal plan to provide efficient logistics services by avoiding factors such as insufficient logistics resources and labor shortages [1, 2, 3, 4]. Accurate demand prediction helps to provide reliability to the delivery process, such as in accepting parcels from customers, sorting and transporting them to delivery stations, and delivering to the recipients on time [5]. There have been many approaches based on different perspectives proposed on logistics demand forecasting. Statistical methods are widely used to learn economic factors affecting increased mail volume. The authors of [6] used the Vector Error Correction (VEC) model based on three economic factors, including Gross Domestic Product (GDP), telecommunication price index, and mail price index, to predict future mail demand. In [7], the elastic coefficient method was used to predict total logistics volume by considering the ratio between the growth rate of total logistics volume and GDP for 21 cities in Southeast Asia. [PERSON] et al. proposed a non-weighted symmetric Savitzky-Golay filter modification of a simple Seasonal Autoregressive Integrated Moving Average (SARIMA) model for forecasting the monthly volume of postal services in the Republic of Serbia and compared with the SARIMA model. The proposed method gave 30% less error than SARIMA [8]. The authors of [9] considered three models, such as Autoregressive Integrated Moving Average (ARIMA), the Holt-Winters decomposition, and Multiple Linear Regression (MLR), to forecast quarterly postal traffic in Portugal. [PERSON] et al. used [PERSON]'s Exponential Smoothing model for predicting the yearly number of postal services in Albania. The model was built on data from 1993 to 2015 and evaluated by prediction of 2016 and 2017 [2]. Recently, machine learning and deep learning models have been becoming more popular and show better performance for predicting logistics demand [10; 11; 12; 13]. [PERSON] et al. proposed the Least Squares Support Vector Machine (LS-SVM) optimized by a genetic algorithm for forecasting logistics demand. They compared the optimized LS-SVM with simple LS-SVM and backpropagation (BP) neural network algorithms on a dataset from 1991 to 2003 in the China statistical yearbook [10]. Another enhanced SVM algorithm optimized by parameters of penalty and radial bases function based on an ant colony algorithm was proposed in [11]. They conducted an experiment using statistics on Qingdao's logistics demand from 1999 to 2017. The improved SVM showed promising results to predict logistics demand. In [12], SARIMA and Long Short-Term Memory (LSTM) were evaluated by predicting the monthly volume of express mail services of international traffic in the Republic of Serbia. The LSTM model gave about 35% smaller Root Mean Square Error (RMSE) than the SARIMA model on 48 monthly observations. The authors of [13] used the LSTM model with two-dimensional input for predicting delivery demand based on a particular area. They evaluated the method on a simulated dataset with nine sub-regions, and the prediction accuracy reached 74.81% on the test dataset. The authors of [5] proposed an MLR-based method to predict the daily demand of parcel logistics. First, delivery stations were clustered by the Self-Organizing Map algorithm. Then MLR was developed for each cluster.