Daily air quality index forecasting based on a mixture of ensemble empirical mode decomposition and ARIMA model

Leyuan Yan; Xin Xu; Yudong Meng

doi:10.1117/12.2674710

2 May 2023 Daily air quality index forecasting based on a mixture of ensemble empirical mode decomposition and ARIMA model

Leyuan Yan, Xin Xu, Yudong Meng

Author Affiliations +

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126420L (2023) https://doi.org/10.1117/12.2674710
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

Air quality indexes (AQI) forecasting plays a vital role in quality of life. Most of AQI prediction methods ignore the nonstationarity of time series, which reducing the accuracy of model forecasts. A novel approach which combines ensemble empirical mode decomposition (EEMD) and autoregressive integrated moving average (ARIMA) model is proposed for AQI forecasting in this paper. Firstly, the AQI sequence is decomposed by EEMD and the noise can be removed by the new threshold method. Secondly, ARIMA model is used to predict the obtained multiple stationary subsequences. Finally, all the separate prediction results are summed to obtain the predicted values of AQI. In terms of the forecasting assessment measures, the proposed model is superior to other methods.

Citation Download Citation

Leyuan Yan, Xin Xu, and Yudong Meng "Daily air quality index forecasting based on a mixture of ensemble empirical mode decomposition and ARIMA model", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126420L (2 May 2023); https://doi.org/10.1117/12.2674710

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