A Comparative Study on the Prediction Effects of Multiple Time - series Models

With the continuous improvement of urbanization, the pollution of NO₂ in urban atmosphere has become increasingly severe. In order to accurately predict the concentration of NO₂ in the atmosphere of Urumqi, this study utilized the SARIMA model, Holt - Winters model, and the intervention model to forecast the NO₂ concentration in the atmosphere of Urumqi from April 2024 to March 2025. Additionally, the fitting performance and prediction accuracy of the three models were compared. Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) were selected as the evaluation indicators for model fitting and accuracy. The results showed that for the optimal SARIMA model, the values of MAE, RMSE (Root Mean Squared Error) and MAPE were , , and , respectively. For the optimal Holt - Winters model, the values of MAE, RMSE, and MAPE were , , and respectively. And for the intervention model, the values of MAE, RMSE, and MAPE were , , and . By comparing the prediction results of the three models, it was concluded that the modified optimal Holt - Winters model had the best prediction performance, followed by the intervention model, while the optimal SARIMA model had the worst prediction performance. Finally, the optimal Holt - Winters model was used to predict the NO₂ concentration in the atmosphere of Urumqi in the coming year, providing rational suggestions for the local government's policy - making and residents' travel arrangements.