# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
## 5 Discussion
### Comparison of Proposed Model with Previous Studies  
There have been many studies in the literature on electric vehicle (EV) sales forecasting, and these studies generally provide general sales forecasts using time series, machine learning, and deep learning methods. However, these studies generally do not distinguish between electric vehicle types and group all sales under a single category. The proposed LSTM-CNN-based model differs from the studies in the literature by providing more detailed and precise sales forecasts with four different output parameters (BEV, HEV, PHEV, and ALL). For example, [PERSON] and [PERSON] (2022) predicted electric vehicle sales with the PCA-GRNN model and found the Multiple R value to be 0.91 [9]. [PERSON] et al. (2022) made an estimate using a relevance vector machine and a Gaussian kernel function and reported the RMSE value as 5.0893 [29]. However, our study found the RMSE value for the ALL parameter to be 2.62, indicating a significantly lower error rate in our model. For example, [PERSON] and [PERSON] (2019) made predictions using NGMB and PSO algorithms, and the MAE value was found to be below 300 vehicles [23]. In our study, the MAE value was calculated as 2.055 for ALL, 3.6705 for BEV, 4.1358 for HEV, and 1.3801 for PHEV. These results show that the proposed model has lower error rates and makes more accurate predictions compared to other models in the literature. Similarly, the MAE value was found to be 4.7119 in the predictions made with the Seq2 Seq model used in [PERSON] et al. (2022) [26], but this value was much lower in our study. When compared with the studies on time series forecasting in the literature, the superiority of the proposed model becomes even more evident. For example, [PERSON] et al. (2017) found the MAPE value as 29.1% and the RMSE value as 16.31 using SSA and VAR models [6]. In our study, these values are much lower, which increases the forecast accuracy of our model. Similarly, in [PERSON] et al. (2022) study, the SVR model was used, and the R-Squared value was found to be 0.82 [30]. In our model, the R-Squared value was calculated as 94.67%, which shows that our model offers higher accuracy compared to other models in the literature. When the studies using deep learning methods are examined, [PERSON] et al. (2023) found the adjusted R-Squared value as 0.82 using deep learning models such as LSTM, GRU, and RNN [31]. With a similar approach, the LSTM-CNN-based model used in our study resulted in higher accuracy rates. Particularly, the predictions across various vehicle categories demonstrate the superior performance of deep learning methods over classical machine learning algorithms. Finally, the MAPE value was found to be 8.83% in the predictions made with the Time-Varying Gray Bernoulli model used in the study of [PERSON] et al. (2023) [24]. However, the MAPE values of the proposed model are much lower, revealing that the prediction accuracy of our model is higher. In addition, [PERSON] et al. (2023) used sentiment analysis and multi-angle feature extraction techniques to estimate electric vehicle sales and analyzed online reviews with the Bi-LSTM model. This model used ISOMAP, LLE, and PCA techniques to extract multi-frequency features and increased the accuracy of the results. This study obtained RMSE 197.01 and MAPE 4.63% as error measures [32]. [PERSON] et al. (2023) used BPNN, RNN, and LSTM models to estimate and report MAPE 159.47, MAE 2.55, and RMSE 15.44 values [33]. [PERSON] et al. (2023) used gray power model (FDGPM) and Bi-LSTM to estimate, and R-Squared was obtained as 0.88 [34]. Compared to these studies, the fact that our study provides detailed estimates for different vehicle types and low error rates shows the superiority of our model. For example, in our study, RMSE for ALL was 2.62, MAPE was 2.83%, and MAE was 1.38, indicating much lower error rates compared to other studies.