# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
## 2 Literature Review
(2023) generated a predictive model that combines sentiment analysis and multi-angle feature extraction techniques to estimate the sales of electric vehicles. The online comments data were utilized in a Bi-directional Long Short-Term Memory (Bi-LSTM) model to estimate the number of EV sales with Isometric Mapping Manifold Learning (ISOMAP), Local Linear Embedding (LLE), and PCA techniques [34]. [PERSON] et al. (2023) generated a prediction model utilizing backpropagation neural network (BPNN), recurrent neural network (RNN), and LSTM neural network techniques, based on online comments on NEVs [35]. [PERSON] et al. (2023) created a multi-factor prediction model by integrating gray relation analysis (GRA), discrete wavelet transforms (DWT), and Bi-LSTM techniques for forecasting electric car sales in 31 provinces in China [36]. [PERSON] and [PERSON] (2023) found the parameters that were used most often in the literature to create EV sales forecasting models. They then used neural networks, Bayesian linear regression, and light gradient boosting machine (LightGBM) methods to create an EV sales forecasting model [37]. [PERSON] et al. (2024) developed a model using an agent-based model and system dynamics to estimate the penetration of passenger EVs and greenhouse gas emissions caused by these vehicles. The study compared the results obtained by solving the developed model under different scenarios [38]. Researchers are increasingly using machine learning approaches for EV sales predictions, having successfully applied them in demand forecasting. [PERSON] and [PERSON] (2022) employed statistical and machine learning methods to predict electric vehicle sales in China and globally. The study developed a prediction model using Principal Component Analysis (PCA) and General Regression Neural Network (GRNN) techniques to forecast sales [9]. [PERSON] et al. (2024) examined the studies in the literature on EV sales forecasting, deter mined the 12 most commonly used factors in the development of forecasting models, and compared the importance of these factors with the DEMATEL method by obtaining expert opinions. Then, they made EV sales forecasts for India using these criteria with ARIMA, XGBoost, Random Forest, and LSTM methods [39]. [PERSON] et al. (2024) developed a forecast model using ARIMA and LSTM methods for NEV vehicle sales in China with data between 2018 and 2023 [40]. [PERSON] et al. (2024) developed a model based on machine learning techniques for the market forecasting of two- and three-wheeled EVs in India. In the study, prediction models were created with linear regression, Support Vector Regression (SVR), LSTM, and neural networks, and the results were compared with the RMSE method [41]. Recent studies have shown that researchers prefer algorithms based on machine learning and deep learning. Table 1 provides a comprehensive summary of research articles in the literature on EV sales forecasting. Examining the data, it is evident that the topic of EV sales forecasting has attracted the interest of researchers, especially in the last two years. Researchers have recently used machine learning and deep learning techniques to develop EV demand forecasting models, as shown in the literature review. The error coefficients for MAPE and RMSE were used to evaluate the prediction models developed in the literature with alternative prediction techniques. This study fills several critical gaps in the literature that existing forecasting models cannot fill. Most studies on electric vehicle (EV) sales forecasting in the literature evaluate