## 2 Literature Review  
From the perspective of learning mechanisms of forecasting models, we can divide them into two categories: conventional forecasting models and machine learning forecasting models. Conventional forecasting models are those that use simple rules or methods to forecast future values, such as the naive method (NM), moving average (MA), autoregressive (AR) and autoregressive integrated moving average (ARIMA), etc. Machine learning forecasting models are those that employ more complex computational methods and model structures to extract underlying patterns from the data, such as multilayer perceptron (MLP), recurrent neural network (RNN), convolutional neural network (CNN) and Transformer, etc. The summary of the literature review is presented in Table 1.  
Among the conventional forecasting models, the naive method is the simplest but most effective time series forecasting method [9]. It takes the actual value at time \(t-1\) as the forecasting value at time \(t\). In actual production, many enterprises choose to use the naive method as the basic forecasting method to guide their operations plan. The naive method is also used as a benchmark for the evaluation of the performance of other forecasting methods [10]. For any designed forecasting model, the method is valid if its accuracy is higher than the naive method's, and vice versa. It is similar to random guess in classification problems. The moving average is another method commonly used to forecast future value [11]. It uses the average of a group of recent actual values to forecast future values, such as demand and capacity, etc. However, this method can only be used when the demand is neither rapid growth nor rapid decline, and there is no seasonal factor. Previous studies investigated optimal MA length for forecasting future demand. Their findings suggest that optimal MA length is related to the frequency of occurrence of the structural change [12]. The autoregressive model is developed from linear regression in regression analysis and used to deal with time series [13]. It uses the historical values of the same variable (\(y_{t-1}\) to \(y_{t-n}\)) to forecast the current \(y_{t}\). Because an autoregression model only uses the historical value of a variable to forecast its future value, it does not use other variables, so it is called autoregressive. Many studies have analysed and improved AR [13; 14; 15; 16]. Furthermore, [PERSON] and [PERSON] integrated AR and MA methods and added an integrated method to put forward the ARIMA time series forecasting model [17]. On this basis, ARIMAX and SARIMA were designed to handle multivariate input data and seasonal input data, respectively. Many studies use ARIMA and its derived models to forecast the future value of the target and obtain acceptable forecasting accuracy [18; 19; 20]. The traditional method is used by many enterprises because of its simple deployment and fast computing speed. However, these methods are difficult to obtain complex influence relationships from a large number of influencing factors, so scholars put forward more complex and effective forecasting models called machine learning (ML) [21]. MLP is a kind of neural network machine learning model which attracts a great deal of attention [22]. It is a fully connected feedforward artificial neural network and has been employed as a benchmark to test the forecasting performance of other forecasting models [23; 24; 25]. MLP was improved by integrating other forecasting models [26; 27; 28; 29]. The concept of deep learning originates from the development of the artificial neural network [30]. MLP with multiple hidden layers can be considered as a deep learning structure [31]. By combining low-level features, deep learning can form more abstract high-level attributes or features to discover distributed feature representations of data [32]. There are many architectures for deep learning, among which RNN is a common architecture. Many complex and well-performing deep learning architectures are based on RNN [33].