## 2 Literature Review
### Deep Learning for Time Series Forecasting  
Recently, with deep learning methods and data analysis development, prediction models based on data mining and deep learning techniques have become increasingly  
Figure 1: Structure of the LSTM [24]. popular. Their prediction effectiveness can be used to judge the quality of these models [27]. Deep learning algorithms have achieved outstanding performance in prediction and detection in various application fields, such as the financial market [28] and natural language processing [29; 30]. Many researchers have used deep learning time series methods to address air pollution prediction problems, and one of the most common methods in deep learning techniques is LSTM. It is widely applied in many areas of time series prediction, especially in air pollution prediction. Previous research shows its success in providing accurate predictions of air pollution parameters. [PERSON] et al. [31] developed an air quality prediction model that combined multiple individual deep learning models. The experiments conducted on the dataset collected from monitoring stations in Beijing showed that LSTM was more accurate than other deep learning models, such as CNNs. Moreover, some researchers have also used the Time2 Vec algorithm in different prediction areas, such as stock prices, electricity consumption, and traffic flow [26]. It has proven its effectiveness and success with LSTM, which was included in constructing the proposed model in this research. In [32], the authors demonstrated that combining the temporal feature extraction capabilities of LSTM with the spatial feature extraction capabilities of CNNs and applying this combined model to diverse data types resulted in improved predictive performance compared to using a single data model. An attention mechanism was also added to CNN-LSTM, capturing the importance of different distinct states over time, which enhances the prediction quality [33]. As demonstrated by [PERSON] et al., the LSTM model achieved the best results compared to other deep learning models [34]. They studied air quality based on the concentrations of tiny particles of PM\({}_{2.5}\) in the air, which seriously impacts health, especially with prolonged exposure. The authors applied several deep learning and machine learning models to predict PM\({}_{2.5}\) concentrations. Among these models, the LSTM model achieved the best prediction regarding evaluation metrics, such as RMSE, MSE, MAE, and R\({}^{2}\). Researchers looked for factors that affect air pollution and found that one of them was vehicle emissions, as explained by [PERSON] et al. [35] when they studied the LSTM approach. LSTM was primarily used to predict the concentrations of NOx, CO\({}_{2}\), O\({}_{3}\), and PM\({}_{2.5}\) in the air at a site in the National Capital Territory of Delhi (NCT-Delhi). Five time series parameters, such as traffic data, pollutant levels, vehicle emissions, and weather conditions, were analyzed using various LSTM models to identify air pollution contributors. The results revealed that meteorological factors significantly affect CO\({}_{2}\) levels, while traffic and emissions data primarily influence PM\({}_{2.5}\) and O\({}_{3}\) concentrations, with both impacting NOx levels. In addition, [PERSON] et al. [27] proposed a new spatiotemporal deep learning model (ST-DMTL) designed for air quality prediction, utilizing data from a Chinese dataset. By employing multi-task learning techniques with RNNs, it categorizes data into six variable types. Experiments affirmed its superiority over LSTM and other advanced deep learning methods in predicting air pollutant concentrations. [PERSON] et al., in [36], used the Time2 Vec algorithm to predict total monthly electricity consumption in China, with the study sample covering from January 2009 to December 2020. This method enhanced the existing transformer model by effectively capturing seasonality and trend components. By incorporating the monthly sequence into the transformer model, the approach achieved more accurate predictions.