In recent years, the frequent occurrence of air pollution incidents has seriously affected people’s health and life. Therefore, PM2.5\documentclass[12pt]{minimal}
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\begin{document}$$_{2.5}$$\end{document}, as the main pollutant, is an important research object of air pollution at present. Effectively improving the prediction accuracy of PM2.5\documentclass[12pt]{minimal}
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\begin{document}$$_{2.5}$$\end{document} volatility makes the PM2.5\documentclass[12pt]{minimal}
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\begin{document}$$_{2.5}$$\end{document} prediction content perfect, which is an important aspect of PM2.5\documentclass[12pt]{minimal}
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\begin{document}$$_{2.5}$$\end{document} concentration research. The volatility series has an inherent complex function law, which drives the volatility movement. When machine learning algorithms such as LSTM (Long Short-Term Memory Network) and SVM (Support Vector Machine) are used for volatility analysis, a high-order nonlinear form is used to fit the functional law of the volatility series, but the time-frequency information of the volatility has not been utilized. Based on EMD (Empirical Mode Decomposition) technique, GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model and machine learning algorithms, a new hybrid PM2.5\documentclass[12pt]{minimal}
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\begin{document}$$_{2.5}$$\end{document} volatility prediction model is proposed in this study. This model realizes time-frequency characteristic extraction of volatility series through EMD technology, and integrates residual and historical volatility information through GARCH model. The simulation results of the proposed model are verified by comparing the samples of 54 cities in North China with the benchmark models. The experimental results in Beijing showed that MAE (mean absolute deviation) of hybrid-LSTM decreased from 0.00875 to 0.00718 compared with LSTM, and hybrid-SVM based on the basic model SVM also significantly improved generalization ability, and its IA (index of agreement) improved from 0.846707 to 0.96595, showing the best performance. The experimental results show that the hybrid model is superior to other considered models in terms of prediction accuracy and stability, which verifies that the hybrid system modeling method is suitable for PM2.5\documentclass[12pt]{minimal}
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\begin{document}$$_{2.5}$$\end{document} volatility analysis.
