# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
## 5 Discussion
### Limitations of the Study and Recommendations for Future Research  
The model and dataset used in this study have some important limitations. These limitations are as follows:  
* The dataset used in the study consists of data specific to the USA only. Therefore, there are uncertainties regarding the applicability of the model to other geographic regions or different economic and demographic conditions. Factors such as the growth dynamics of the electric vehicle market in different countries, the pace of infrastructure development, and economic conditions may affect the predictive accuracy of the model. Therefore, testing the model with datasets from other countries or regions will be an important step in increasing its generalizability.
* The dataset only includes monthly data between 2015 and 2022. This limited time frame may limit the performance of the model in long-term forecasting. In particular, it is uncertain how well the model can predict future market trends due to rapid changes in EV technology and market dynamics. To overcome this limitation, future studies should consider using longer-term datasets and testing the performance of the model over different time periods.
* Although the independent variables used in our study (gasoline and diesel prices, electricity prices, renewable energy production, number of charging stations, etc.) are important factors influencing the demand for electric vehicles, they do not take into account macroeconomic and social factors that may lie outside these variables. For example, factors such as government incentives, changes in consumer preferences, or technological advances may affect the performance of the model. Working with a wider set of variables may make the model more sensitive to real-world variations.
* The LSTM and CNN models used in our study have several limitations. First, the model assumptions and parameter choices may not fully reflect all dimensions of a complex system, such as electric vehicle demand. In addition, the effectiveness of structural choices such as hyperparameter settings and the number of layers used, which can affect the performance of the model, have not been fully investigated. Although the hyperparameter optimization of the model was carried out, further analysis is required to determine the extent to which the selected parameters can be generalized to different datasets and different time periods. In future research, ablation studies can be conducted to verify the effectiveness of the model's hyperparameters and architecture. Such studies will allow the model to reach a more optimized structure by measuring the contribution of each component to the model's performance.
* Deep learning models generally perform better on large datasets. However, as the dataset used in this study is relatively limited, the performance of the model cannot be fully tested. Retraining the model with large datasets and using more complex artificial neural network architectures has the potential to improve its performance.  
In light of these limitations, it will be important for future studies to test the model in different geographical regions, with larger datasets and with more diverse independent variables, in order to increase the generalizability of the model. In addition, examining how the model performs under different scenarios will help us to evaluate the results of this study in a broader context.