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An adaptive decomposition and ensemble model for short-term air pollutant concentration forecast using ICEEMDAN-ICA

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  • Xiao, Yu-jie
  • Wang, Xiao-kang
  • Wang, Jian-qiang
  • Zhang, Hong-yu

Abstract

Precise short-term atmospheric pollutant concentration forecasting is significant for providing early warning information against harmful pollutants. Many studies on pollutant concentration prediction have proven the excellence of decomposition and ensemble models. However, in most of those studies, the training and test sets are divided based on the decomposition results rather than the original time series. In such decomposition and ensemble framework, future information is used for prediction, which is impractical. Furthermore, a significant boundary effect in the decomposition results is also a serious problem. Thus, this study develops an adaptive forecasting scheme aiming at ensuring the model practicality and adapting to the boundary effect. This study also introduces independent component analysis (ICA) to help extract the hidden information of the original series and improves the ability to screen influential variables. Finally, an adaptive decomposition and ensemble model combined with ICA is developed. Using data collected from Beijing Shunyi station, a case study and two comparative experiments are conducted, through which the contribution of the methods used in the proposed model and the superior performance of the model are demonstrated.

Suggested Citation

  • Xiao, Yu-jie & Wang, Xiao-kang & Wang, Jian-qiang & Zhang, Hong-yu, 2021. "An adaptive decomposition and ensemble model for short-term air pollutant concentration forecast using ICEEMDAN-ICA," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    • Handle: RePEc:eee:tefoso:v:166:y:2021:i:c:s0040162521000871
    DOI: 10.1016/j.techfore.2021.120655
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    References listed on IDEAS

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    Cited by:

    1. Kraus, Sascha & Kumar, Satish & Lim, Weng Marc & Kaur, Jaspreet & Sharma, Anuj & Schiavone, Francesco, 2023. "From moon landing to metaverse: Tracing the evolution of Technological Forecasting and Social Change," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    2. Wang, Zicheng & Gao, Ruobin & Wang, Piao & Chen, Huayou, 2023. "A new perspective on air quality index time series forecasting: A ternary interval decomposition ensemble learning paradigm," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    3. Tian, Zhirui & Wang, Jiyang, 2023. "A wind speed prediction system based on new data preprocessing strategy and improved multi-objective optimizer," Renewable Energy, Elsevier, vol. 215(C).
    4. Xu, Kunliang & Niu, Hongli, 2022. "Do EEMD based decomposition-ensemble models indeed improve prediction for crude oil futures prices?," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    5. Liu, Ying Lin & Zhang, Jing Jie & Fang, Yan, 2023. "The driving factors of China's carbon prices: Evidence from using ICEEMDAN-HC method and quantile regression," Finance Research Letters, Elsevier, vol. 54(C).
    6. Zhang, Yagang & Kong, Xue & Wang, Jingchao & Wang, Hui & Cheng, Xiaodan, 2024. "Wind power forecasting system with data enhancement and algorithm improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 196(C).
    7. Yue-Jun Zhang & Han Zhang & Rangan Gupta, 2021. "Forecasting the Artificial Intelligence Index Returns: A Hybrid Approach," Working Papers 202182, University of Pretoria, Department of Economics.

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