## 1 Introduction  
The National Human Activity Pattern Survey (NHAPS) showed that respondents spent an average of 87% of their time in enclosed buildings, while the remaining 6% was spent in cars and 7% outdoors [1]. Indoor air pollutants (such as CO\({}_{2}\) and HCHO) can be harmful to human health, leading to drowsiness, headaches, or reduced concentration [2]. The primary indoor activity area for students is the classroom. Previous studies have shown that indoor air quality in classrooms affects students' learning efficiency and concentration and may also have long-term effects on the physical health of both students and teachers [3, 4]. Poor indoor air quality can increase student absences, decrease test scores, and even cause people to develop sick building syndrome [5, 6, 7]. [PERSON] et al. [8] showed, through measurements, that indoor CO\({}_{2}\) levels are a good indicator for investigating air pollutants in classrooms. The Hygienic Standard for Carbon Dioxide Indoor Air states that the standard for indoor CO\({}_{2}\) concentration is 1000 ppm [9]. [PERSON] et al. [10] noted through controlled experiments on the threshold standard for CO\({}_{2}\) concentration (500 ppm to 1500 ppm) that both high and low CO\({}_{2}\) concentrations have an impact on people's health and productivity. Additionally, [PERSON] et al. [11] showed that working in a room with CO\({}_{2}\) at a high concentration of 5000 ppm leads to physical and psychological discomfort, as well as a decrease in cognitive performance. Moreover, it was found that students' task performance speed, test scores, and attendance increased when the CO\({}_{2}\) concentration decreased [12; 13]. A prevalent approach to managing indoor CO\({}_{2}\) concentrations is through ventilation [14]. However, there are identified ventilation shortcomings in American educational institutions [15]. Assessing the necessity of activating the ventilation system preemptively to address classroom CO\({}_{2}\) levels poses a significant challenge. Therefore, predicting CO\({}_{2}\) concentrations in classrooms is necessary to create a comfortable learning environment for students. Predicting CO\({}_{2}\) concentrations with high accuracy can provide valuable data to support corresponding ventilation measures in classrooms. Numerous scholars have explored various approaches for predicting indoor CO\({}_{2}\) concentrations. One popular approach for making these predictions in classroom environments relies on traditional mathematical or physical principles. [PERSON] et al. [16] employed a mass balance equation to create a dynamic calculator that visualizes the impact of the indoor volume, exhalation rate, air exchange rate, carbon dioxide exhalation rate, and initial CO\({}_{2}\) concentration in the environment on the accumulation and decay of CO\({}_{2}\) in a room. [PERSON] et al. [17] developed an integrated equation based on factors such as the initial CO\({}_{2}\) concentration, air exchange rate, per capita occupancy, and CO\({}_{2}\) exhalation rate for individuals engaged in different activity intensities. The proposed model has the potential to be used when analyzing indoor air quality. [PERSON] et al. [18] controlled variables such as the number of students, physical characteristics, and activity intensity in a faculty building at Sakarya University to validate their developed mathematical model and simulator software. This software can be used to analyze the variation in CO\({}_{2}\) concentration under different indoor conditions considering factors such as various ventilation methods, staffing levels, window and door properties, and room shapes and sizes. [PERSON] et al. [19] utilized a double exponential smoothing model to predict CO\({}_{2}\) emissions in 50 states and in the U.S. transportation sector and showed that this model is supported by validity tests for pseudo out-of-sample predictions.