Prediction of PM\({}_{2.5}\) Concentration Based on Deep Learning for High-Dimensional Time Series  
[PERSON]  
1 School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China; [EMAIL_ADDRESS] (J.H. ); [EMAIL_ADDRESS] (C.-B.H. ); [EMAIL_ADDRESS] (F.S. ); hhd  
the burden of air pollution on the global economy is about USD 225 billion per year [6]. Hence, it becomes imperative to accurately and reliably predict PM\({}_{2.5}\) concentration to evaluate air pollution severity effectively, thereby enabling measures to be implemented to mitigate PM\({}_{2.5}\) levels and consequently reduce the associated health risks. Traditional machine learning models such as Autoregressive Integrated Moving Average (ARIMA), Support Vector Machine (SVM), Markov model (HMM), and Random Forest (RF) have been used to predict PM\({}_{2.5}\) concentration [7; 8; 9; 10; 11]. However, due to the nonlinear relationship between the change in PM\({}_{2.5}\) concentration and external factors, these models cannot accurately predict PM\({}_{2.5}\) concentration. Neural network-based machine learning models effectively predict PM\({}_{2.5}\) concentration [12]. Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) have been used to learn complex nonlinear relationships in PM\({}_{2.5}\) concentration time series data to improve prediction accuracy [13; 14]. However, RNNs face challenges such as the vanishing gradient and exploding gradient issues when dealing with long sequences. To more effectively address the vanishing gradient issue and the long-term dependency challenge in RNN, Long Short-Term Memory neural network (LSTM) [15] and gated recurrent network (GRU) [16] have been proposed to solve this problem. Since then, [PERSON] et al. have proposed a balanced social LSTM (BS-LSTM) for predicting PM\({}_{2.5}\) concentration in cities [17]. [PERSON] and [PERSON] introduced an enhanced loss function (ELF) to decrease the error and improve the accurate prediction of daily PM\({}_{2.5}\) concentration in India [18]. [PERSON] et al. proposed a model based on inverse convolution and LSTM so that the model can extract the spatial feature correlation of atmospheric pollutant concentration data to realize the accurate prediction of PM\({}_{2.5}\) concentration [19]. [PERSON] et al. established a new hybrid model combining CNN, LSTM, and GRU for predicting PM\({}_{2.5}\) concentration in Seoul, South Korea [20]. In addition, [PERSON] and [PERSON] developed an LSTM model that incorporates Principal Component Analysis (PCA) and an attention mechanism. This approach mitigated the correlation effects between indicators, simplified the model, and yielded improved prediction results in their experiments [21]. However, as the length of time series data increases, LSTM and GRU models may deteriorate into random guessing. Compared to the LSTM network, the GRU network features a simpler architecture, reduced computational complexity, and quicker training [22]. Unlike LSTM and GRU, the Temporal Convolutional Network (TCN) offers the more comprehensive parallel processing of information, a simpler structure, fewer parameters, and is better suited for time series modeling [23]. The TCN has been shown to be more effective than LSTMs for time series prediction tasks [24]. Since PM\({}_{2.5}\) concentration is measured at specific monitoring stations, traditional methods may struggle to generalize effectively across different locations and environmental conditions. The advantage of hybrid deep learning methods lies in their ability to integrate multiple data sources and modeling techniques, capturing the complex relationships between meteorological factors, geographic variations, and pollutant dispersion patterns. This not only improves the accuracy and generalization capability of PM\({}_{2.5}\) predictions but also provides more reliable support for air quality management and public health protection.