# Prediction and Comparison of In-Vehicle CO\({}_{2}\) Concentration Based on ARIMA and LSTM Models
## 5 Conclusions and Future Work  
This research focuses on the concentration of CO\({}_{2}\) in vehicles as its research subject. Through field tests, data on the CO\({}_{2}\) concentration within vehicles were obtained and established using both ARIMA and LSTM prediction models for forecasting changes in CO\({}_{2}\) concentration. The accuracy of these two models was analyzed and compared, with differences in performance discussed. The main conclusions are as follows:  
1. The measured data indicate that with closed vehicle windows and internal circulation turned on, the concentration of CO\({}_{2}\) in a vehicle increases proportionally to driving time. Under all test conditions, the average CO\({}_{2}\) concentration within 20 min exceeds the standard limit of 1000 ppm. During driving, it is necessary to improve vehicle ventilation by opening windows, switching to outside air mode, and using other methods. 2. Both the ARIMA and LSTM models are capable of accurately predicting changes in the CO\({}_{2}\) concentration within a vehicle. However, the MAPE and RMSE values obtained from the ARIMA model's predictions are lower than those produced by the LSTM model. Therefore, the ARIMA model is more accurate in predicting changes in the CO2 concentration within a vehicle compared to the LSTM model. 3. The prediction performance of LSTM is not always better than the ARIMA model, and the two models need to be selected according to the characteristics of the data. When the amount of data used for training reaches 1000, it is better to use LSTM, and it is more appropriate to use ARIMA when the amount of data is within 100, but comparative analysis is required when the amount of data is within 100-1000. Predicting the change in CO2 can remind the people in a vehicle to take control measures before the concentration exceeds the standard. This research provides a critical theoretical basis for the future selection of early warning prediction models for in-vehicle CO2 concentrations and for establishing efficient in-vehicle ventilation control systems. In future research, we should develop an in-vehicle ventilation management system that integrates predictive capabilities with control functionalities. Furthermore, we should comprehensively consider the influencing factors of in-vehicle CO2 concentration and develop a time series prediction model that incorporates multiple input variables to investigate whether these models can provide more accurate predictions of in-vehicle CO2 concentration. **Author Contributions:** Supervision, conceptualization, methodology, writing--review, J.H. ; design and conducting of experiments, original draft, H.L. ; algorithm implementation, writing--review and editing, Z.Q. All authors have read and agreed to the published version of the manuscript. **Funding:** Thanks to the Guangxi Natural Science Foundation's \"Study on the thermal adaptation behavior characteristics of opening/closing windows and turning on/off air conditioners of mixed cooling building residents in Guangxi area\" (Funder: Associate Prof. [PERSON], Funding number: 2018 GXNSFAA281212) and Guangxi Public Security Department Research Project \"Research on the Construction of Knowledge Graph in the Field of Telecom Fraud\" (Funder: Senior Engineer. [PERSON], Funding number: 2023 GAZD018) for their support of this research. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest.