IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v15y2018i9p1941-d168135.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/15/9/1941/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/15/9/1941/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Wenling Liu & Ziping Xu & Tianan Yang, 2018. "Health Effects of Air Pollution in China," IJERPH, MDPI, vol. 15(7), pages 1-15, July.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chongshi Gu & Xiao Fu & Chenfei Shao & Zhongwen Shi & Huaizhi Su, 2020. "Application of Spatiotemporal Hybrid Model of Deformation in Safety Monitoring of High Arch Dams: A Case Study," IJERPH, MDPI, vol. 17(1), pages 1-25, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ding, Yishan, 2018. "A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting," Energy, Elsevier, vol. 154(C), pages 328-336.
    2. Safari, Ali & Davallou, Maryam, 2018. "Oil price forecasting using a hybrid model," Energy, Elsevier, vol. 148(C), pages 49-58.
    3. Lin, Ling & Jiang, Yong & Xiao, Helu & Zhou, Zhongbao, 2020. "Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(C).
    4. Zhao, Geya & Xue, Minggao & Cheng, Li, 2023. "A new hybrid model for multi-step WTI futures price forecasting based on self-attention mechanism and spatial–temporal graph neural network," Resources Policy, Elsevier, vol. 85(PB).
    5. Taiyong Li & Yingrui Zhou & Xinsheng Li & Jiang Wu & Ting He, 2019. "Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors," Energies, MDPI, vol. 12(19), pages 1-25, September.
    6. Jiang Wu & Yu Chen & Tengfei Zhou & Taiyong Li, 2019. "An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting," Energies, MDPI, vol. 12(7), pages 1-23, April.
    7. Chen, Yanhui & Zhang, Chuan & He, Kaijian & Zheng, Aibing, 2018. "Multi-step-ahead crude oil price forecasting using a hybrid grey wave model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 98-110.
    8. Fan, Liwei & Pan, Sijia & Li, Zimin & Li, Huiping, 2016. "An ICA-based support vector regression scheme for forecasting crude oil prices," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 245-253.
    9. Lean Yu & Yueming Ma, 2021. "A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting," Energies, MDPI, vol. 14(15), pages 1-26, July.
    10. Yang, Dongchuan & Guo, Ju-e & Li, Yanzhao & Sun, Shaolong & Wang, Shouyang, 2023. "Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach," Energy, Elsevier, vol. 263(PA).
    11. Wang, Jun & Cao, Junxing & Yuan, Shan & Cheng, Ming, 2021. "Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the PSO-ALS-optimized GRU network," Energy, Elsevier, vol. 233(C).
    12. Cheng, Fangzheng & Li, Tian & Wei, Yi-ming & Fan, Tijun, 2019. "The VEC-NAR model for short-term forecasting of oil prices," Energy Economics, Elsevier, vol. 78(C), pages 656-667.
    13. Li, Jinchao & Zhu, Shaowen & Wu, Qianqian, 2019. "Monthly crude oil spot price forecasting using variational mode decomposition," Energy Economics, Elsevier, vol. 83(C), pages 240-253.
    14. Zhu, Jiaming & Wu, Peng & Chen, Huayou & Liu, Jinpei & Zhou, Ligang, 2019. "Carbon price forecasting with variational mode decomposition and optimal combined model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 140-158.
    15. Taiyong Li & Min Zhou & Chaoqi Guo & Min Luo & Jiang Wu & Fan Pan & Quanyi Tao & Ting He, 2016. "Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels," Energies, MDPI, vol. 9(12), pages 1-21, December.
    16. Tao XIONG & Chongguang LI & Yukun BAO, 2017. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(3), pages 136-148.
    17. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    18. Sun, Shaolong & Wang, Shouyang & Wei, Yunjie, 2019. "A new multiscale decomposition ensemble approach for forecasting exchange rates," Economic Modelling, Elsevier, vol. 81(C), pages 49-58.
    19. Ju, Keyi & Su, Bin & Zhou, Dequn & Zhang, Yuqiang, 2016. "An incentive-oriented early warning system for predicting the co-movements between oil price shocks and macroeconomy," Applied Energy, Elsevier, vol. 163(C), pages 452-463.
    20. Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong & Liu, Zhenkun, 2021. "Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection," Applied Energy, Elsevier, vol. 301(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:15:y:2018:i:9:p:1941-:d:168135. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.