## 2 Literature Review of Sales Prediction Methods  
The existing sales prediction methods are associated with parameter prediction methods based on time series, machine learning, and deep learning models.  
The parameter prediction methods based on time series can be mainly divided into two categories: traditional statistical methods and autoregression methods. Traditional statistical methods consider that time series have regularity and can be postponed reasonably along the trend. [PERSON] used the exponential smoothing method to solve sales forecasting problems in retail stores [10]. [PERSON] used the single exponential smoothing and double exponential smoothing methods in the case of product selling prediction for the XYZ store [11]. [PERSON] presented an improved exponential smoothing method in the M3 competition and achieved good results [12]. [PERSON] proposed that the autoregressive method is more accurate in stationarity time series prediction [13]. The autoregressive method is also used for supply chain development; for example, [PERSON] [14] proposed the ARIMA method for customer demand prediction. [PERSON] [15] then used the ARIMA model to analyze the relationship between prediction accuracy and inventory. Later, [PERSON] [16] proposed hybrid models, e.g., ARIMA-RF and ARIMA-BCART, for wind power generation prediction. [PERSON] [17] developed a combined model based on regression and time series methods for battery charging time prediction. In essence, the parameter prediction methods based on time series can only capture linear relations, and they are inefficient in analyzing nonlinear relations. The parameter prediction methods also require stationary time series data as input. In a stationary time series, its mean, variance, and covariance do not change over time. Thus, a stationary time series has no trend and no periodic changes. However, the data in sales prediction for e-commerce are seasonal, hence increasing the complexity of using the parameter prediction methods. Additionally, the parameter prediction methods based on time series are usually only suitable for single variable prediction; meanwhile, in e-commerce sales scenarios, the factors are diverse.  
Machine learning and deep learning methods have been recently used for multivariable prediction studies [18; 19]. In the application of machine learning, [PERSON] [20] proposed the ForeXGBoost model for vehicle sales prediction. Gradient Boosting Machine (GBM) and Light Gradient Boosting Machine (LightGBM) are also widely applied in sales forecasting [21]. Some other sales prediction methods are related to Support Vector Machine (SVM) [22] and Extreme Learning Machine (ELM) [23; 24]. In the application of deep learning, [PERSON] [25] used a backpropagation neural network based on an artificial immune system to address the problem of sales prediction with the large influence of category variables and proved the accuracy of the algorithm. [PERSON] [26] presented a meta-learning framework based on newly developed deep convolutional neural networks in retail sales prediction. Long Short-Term Memory (LSTM), the Recurrent Neural Network (RNN), and Gated Recurrent Units (GRU) are also widely used deep learning models for sales prediction [27; 28; 29].  
Compared with other prediction methods, deep learning models have higher accuracy but a slow calculation speed and poor interpretability.  
While managing complex sales prediction problems, a single prediction model does not always provide a satisfactory output; hence, many studies are developed using a combination method [30]. The combination method is able to harmonize the advantages of the combined models to improve the prediction accuracy. Deep learning models are always chosen in such combinations due to their high precision in solving complex problems [31; 32]. For example, [PERSON] proposed a combined model based on the ARIMA model and the LSTM neural network to improve the accuracy of sales forecasting for manufacturing companies [33].