## 2 Literature Review  
We divide the time-series prediction model into conventional statistical models, machine learning models, and hybrid models that combine both [7; 18]. In general, the time scales of forecasting can be divided into three categories: short-term forecasts (from one hour up to one week), mid-term forecasts (from one month up to one year), and long-term forecasts (more than one year); these are also respectively referred to as short-term horizon, mid-term horizon, and long-term horizon. Most works use the short-term horizon since it achieves higher accuracy than the other types [7; 8; 19]. Conventional statistical methods include linear regression, autoregressive moving average, general exponential technique, autoregressive integrated moving average (ARIMA), and seasonal ARIMA (SARIMA) [7; 8; 20]. The ARIMA models, developed by [PERSON] and [PERSON] [21], are the most frequently used, and these models constitute a widely used class of linear models for univariate time series. The ARIMA model relies on several criteria to identify an optimal parameter setting, leading to disadvantages of manual selection and a considerable amount of computation time [11]. To overcome this problem, [PERSON] et al. [11] used the auto ARIMA model in the R forecast package. The auto ARIMA models are prone to generating large trend errors when there is a change in the trend near the cutoff period; in that case, they fail to capture any seasonality. In another paper, [PERSON] and [PERSON] [22] used the Prophet model, which is a time series prediction package developed by Facebook. Prophet is a modular regression model with interpretable parameters that can be intuitively adjusted by analysts with domain knowledge about the time series. Machine learning models are currently being deployed in a variety of applications because of their superior ability to handle complex input and output relationships. For example, the artificial neural network (ANN) model has attracted substantial attention since the mid-1980s as a powerful computational tool for time series forecasting problems. The support vector machine (SVM) model has been used in a wide variety of applications due to its powerful generalization capability and its margin maximization mechanisms. Recently, deep learning has emerged as an effective solution due to its powerful representation learning ability. The recurrent neural network (RNN) model is a kind of folded neural network that is considered to be useful for time series data analytics because it has dependencies among points in the type of data and it is designed to model temporal dependencies. The RNN method can be distinguished from feed-forward neural networks by its operation [23]. However, a vanilla RNN model suffers from the problem of exploding and vanishing gradients. To improve the memorization of RNN and better control the information flow, [PERSON] and [PERSON] [24] proposed the long short-term memory (LSTM) method, which adds the gate mechanism inside the RNN layers to control the information flow. With these improvements, the standard LSTM model can recognize and learn long-term dependencies [23]. The standard LSTM model is evolving, with several different variations--improved LSTM versions, such as LSTM with a time gate added to the LSTM (phased LSTM) model [25]); augmented LSTM [26]; LSTM with multivariate fuzzy technology (MF-LSTM [23]); LSTM with an added attention layer [10]; RNN-based intensified LSTM [12]--have all recently showed good performance. Hybrid models, which combine statistical and machine learning models in various ways, are currently being investigated for time series prediction [5; 6; 7; 8; 18]. [PERSON] et al. [27] used a combination of ARIMA, artificial neural networks (ANN), and fuzzy logic to predict exchange rates and gold prices. [PERSON] [28] used a combination of the ARIMA method and the Kalman filter to predict rice prices, which are time-series data. In the study by [PERSON] et al.