# Prediction and Comparison of In-Vehicle CO\({}_{2}\) Concentration Based on ARIMA and LSTM Models
## 1 Introduction
Though these physically driven models can ensure a certain level of accuracy, they also have some limitations: determining the parameters required by the model is difficult, and the assumptions necessary for establishing the model can result in inaccurate calculation results. The increase in CO\({}_{2}\) concentration is directly proportional to the duration of driving time when using the RC ventilation mode, exhibiting typical characteristics of time series data, so it can be considered as a time series dataset [23; 28]. This illustrates the feasibility of using a time series prediction model for forecasting the in-vehicle CO\({}_{2}\) concentration. The autoregressive integrated moving average model (ARIMA) is a classical time series forecasting model that falls under the umbrella of traditional statistical analysis methods. Its ability to analyze seasonality, periodicity, and trends in data make it highly versatile with accurate predictive effect. It has been utilized for morbidity prediction in medical research [29; 30], commodity price and stock price forecasting in finance [31; 32; 33; 34], as well as for applications in geology [35; 36], transportation [37; 38], electricity [39; 40], sound recognition [41], atmospheric environment research [42], and other disciplines. In recent years, the rapid development of big data and artificial intelligence has led to long short-term memory (LSTM), a modified version of a recurrent neural network (RNN), becoming a prominent research topic. Due to its powerful feature extraction capability, LSTM has become a common method for analyzing time series data [43; 44]. LSTM is proficient in capturing long-term dependencies and non-linear relationships from historical data, thereby enabling accurate prediction of future changes [45]. The LSTM also has a wide range of applications: it has been successfully applied to prediction in various fields such as medicine [30; 46; 47; 48], finance [49; 50], transportation [51; 52], acoustics [53], hydrology [54], and marine science [55]. From the above research, it is evident that ARIMA and LSTM share similar application areas. Therefore, numerous studies have applied both models to the same dataset to compare the differences in prediction accuracy. [PERSON] et al. [56] predicted the number of upper respiratory illnesses in Taiwan, China. [PERSON] and [PERSON] [57] used stock price data from the New York Stock Exchange and made predictions. Both studies found that LSTMs exhibited superior predictive performance. [PERSON] and [PERSON] [58] comparedthe accuracy of the two models in predicting wind speed. The root mean squared error (RMSE) values of LSTM and ARIMA were 3.423 and 3.124 respectively, and the mean absolute error (MAE) values were 2.772 and 2.457 respectively. From these three studies, it was found that LSTM prediction accuracy was higher than that of ARIMA. However, some scholars have discovered that ARIMA exhibits higher prediction accuracy than LSTM: [PERSON] et al. (2018) conducted a comparison of the predictive accuracy between ARIMA and LSTM models for monkeypox incidence in the United States. The results showed that ARIMA had lower RMSE and MAE values of 100.38 and 80.79, respectively, compared to LSTM's values of 208.58 and 193.19. Similarly, [PERSON] and [PERSON] (2018) predicted quarterly GDP changes in China using ARIMA, with an RMSE value of 218.203, which was lower than LSTM's value of 609.356. [PERSON] et al. (2018) have demonstrated that ARIMA outperforms other methods in predicting oil production. Other scholars have utilized both LSTM and ARIMA models to predict price changes in more than 70 types of vegetables, with the proportion of mean absolute percentage error (MAPE) values less than 10% being 0.436 and 0.694, respectively ([PERSON] et al., 2018). In addition, some researchers have found that each of the two models has its own advantages. [PERSON] et al.