## 1 Introduction
Compared with the ARIMA and Random Forest (RF) algorithms, their proposed method showed more accurate results on the most clustered regions. [PERSON] investigated demand prediction methods, including regression analysis, RF, and neural network [3]. [PERSON] et al. used a GM (1, 1) model and BP neural network with two hidden layers to predict logistics demand in Guangdong province from 2000 to 2019. As a result, BP neural network predicted better than the GM (1, 1) model [4]. In general, there are few studies on peak-period prediction for postal logistics, and previous studies addressed mid-to-long-term mail volume forecasting methods. However, studies of short-term mail volume prediction for postal logistics are needed to make short-term plans in terms of supporting the normal operation of logistics resources by providing fast detection of trend changes in mail volume. Demand prediction for short-term periods is also important for Korea Post, which provides next-day parcel delivery service, unlike ordinary mail, which can be delivered within three days from the date of acceptance. Previously, about a month or two before the peak periods, each mail center or logistics center established short-term parcel volume forecasts and resource operation plans based on past experiences, which resulted in insufficient logistics resources and staffing. The accurate prediction of peak period is one of the key factors for providing reliable services to the public. Thus, this study considers the prediction of sharp changes in logistics services over a particular period in special holidays rather than the usual period. We have proposed a peak-period prediction method for parcel logistics to improve resource operation of sorting centers. The proposed method is established by a deep learning-based ensemble method, consisting of two Multilayer Perceptron (MLP) models combined by weights to improve performance. First, the postal parcel volume-based features are analyzed; next, several features are extracted by factors, including calendar, internal, and external. Second, the first MLP model is trained to predict the total parcel volume using the external features for a given period, while the second MLP model is developed using the internal features. The internal prediction model enhances the training dataset using the Variational Autoencoder (VAE) model to prevent performance degradation. In the end, the proposedensemble model is constructed based on the combination of the internal and external MLP models with a weight regulation for peak-period prediction. The experimental study was conducted on parcel volume datasets from 25 mail centers in South Korea, and the proposed method shows the superiority in prediction performance compared with RF, Least Absolute Shrinkage and Selection Operator (LASSO), MLR, Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and LSTM models. The remaining part of the paper is organized as follows. Section 2 details the proposed ensemble method constructed by internal and external feature-based peak-period prediction models. The experimental study is described and discussed in Section 3. The conclusion of the presented study is in Section 4.