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A Hybrid Forecasting Approach to Air Quality Time Series Based on Endpoint Condition and Combined Forecasting Model

Author

Listed:
  • Jiaming Zhu

    (School of Mathematical Sciences, Anhui University, Hefei 230601, Anhui, China)

  • Peng Wu

    (School of Mathematical Sciences, Anhui University, Hefei 230601, Anhui, China)

  • Huayou Chen

    (School of Mathematical Sciences, Anhui University, Hefei 230601, Anhui, China)

  • Ligang Zhou

    (School of Mathematical Sciences, Anhui University, Hefei 230601, Anhui, China)

  • Zhifu Tao

    (School of Economics, Anhui University, Hefei 230601, Anhui, China)

Abstract

Air pollution forecasting plays a vital role in environment pollution warning and control. Air pollution forecasting studies can also recommend pollutant emission control strategies to mitigate the number of poor air quality days. Although various literature works have focused on the decomposition-ensemble forecasting model, studies concerning the endpoint effect of ensemble empirical mode decomposition (EEMD) and the forecasting model of sub-series selection are still limited. In this study, a hybrid forecasting approach (EEMD-MM-CFM) is proposed based on integrated EEMD with the endpoint condition mirror method and combined forecasting model for sub-series. The main steps of the proposed model are as follows: Firstly, EEMD, which sifts the sub-series intrinsic mode functions (IMFs) and a residue, is proposed based on the endpoint condition method. Then, based on the different individual forecasting methods, an optimal combined forecasting model is developed to forecast the IMFs and residue. Finally, the outputs are obtained by summing the forecasts. For illustration and comparison, air quality index (AQI) data from Hefei in China are used as the sample, and the empirical results indicate that the proposed approach is superior to benchmark models in terms of some forecasting assessment measures. The proposed hybrid approach can be utilized for air quality index forecasting.

Suggested Citation

  • Jiaming Zhu & Peng Wu & Huayou Chen & Ligang Zhou & Zhifu Tao, 2018. "A Hybrid Forecasting Approach to Air Quality Time Series Based on Endpoint Condition and Combined Forecasting Model," IJERPH, MDPI, vol. 15(9), pages 1-19, September.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:9:p:1941-:d:168135
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    References listed on IDEAS

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    1. Yu, Lean & Wang, Zishu & Tang, Ling, 2015. "A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting," Applied Energy, Elsevier, vol. 156(C), pages 251-267.
    2. Jianzhou Wang & Tong Niu & Rui Wang, 2017. "Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model," IJERPH, MDPI, vol. 14(3), pages 1-33, March.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Wenling Liu & Ziping Xu & Tianan Yang, 2018. "Health Effects of Air Pollution in China," IJERPH, MDPI, vol. 15(7), pages 1-15, July.
    5. He, Kaijian & Yu, Lean & Lai, Kin Keung, 2012. "Crude oil price analysis and forecasting using wavelet decomposed ensemble model," Energy, Elsevier, vol. 46(1), pages 564-574.
    6. Ling Tang & Wei Dai & Lean Yu & Shouyang Wang, 2015. "A Novel CEEMD-Based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(01), pages 141-169.
    7. Che, JinXing & Wang, JianZhou, 2014. "Short-term load forecasting using a kernel-based support vector regression combination model," Applied Energy, Elsevier, vol. 132(C), pages 602-609.
    8. Song, Jingjing & Wang, Jianzhou & Lu, Haiyan, 2018. "A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 643-658.
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