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Using grey Holt–Winters model to predict the air quality index for cities in China

Author

Listed:
  • Lifeng Wu

    (Hebei University of Engineering)

  • Xiaohui Gao

    (Hebei University of Engineering)

  • Yanli Xiao

    (Hebei University of Engineering)

  • Sifeng Liu

    (De Montfort University)

  • Yingjie Yang

    (De Montfort University)

Abstract

The randomness, non-stationarity and irregularity of air quality index series bring the difficulty of air quality index forecasting. To enhance forecast accuracy, a novel model combining grey accumulated generating technique and Holt–Winters method is developed for air quality index forecasting in this paper. The grey accumulated generating technique is utilized to handle non-stationarity of random and irregular data series and Holt–Winters method is employed to deal with the seasonal effects. To verify and validate the proposed model, two monthly air quality index series from January in 2014 to December in 2016 collected from Shijiazhuang and Handan in China are taken as the test cases. The experimental results show that the proposed model is remarkably superior to conventional Holt–Winters method for its higher forecast accuracy.

Suggested Citation

  • Lifeng Wu & Xiaohui Gao & Yanli Xiao & Sifeng Liu & Yingjie Yang, 2017. "Using grey Holt–Winters model to predict the air quality index for cities in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(2), pages 1003-1012, September.
    • Handle: RePEc:spr:nathaz:v:88:y:2017:i:2:d:10.1007_s11069-017-2901-8
    DOI: 10.1007/s11069-017-2901-8
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    References listed on IDEAS

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    1. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    2. Ferbar Tratar, Liljana & Mojškerc, Blaž & Toman, Aleš, 2016. "Demand forecasting with four-parameter exponential smoothing," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 162-173.
    3. Maria Ikram & Zhijun Yan & Yan Liu & Weihua Qu, 2015. "Seasonal effects of temperature fluctuations on air quality and respiratory disease: a study in Beijing," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(2), pages 833-853, November.
    4. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz & Varela Repolho, Hugo Miguel, 2017. "Air transportation demand forecast through Bagging Holt Winters methods," Journal of Air Transport Management, Elsevier, vol. 59(C), pages 116-123.
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    Cited by:

    1. Wu, Wenqing & Ma, Xin & Zeng, Bo & Wang, Yong & Cai, Wei, 2019. "Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model," Renewable Energy, Elsevier, vol. 140(C), pages 70-87.
    2. Weijie Zhou & Huihui Tao & Huimin Jiang, 2022. "Application of a Novel Optimized Fractional Grey Holt-Winters Model in Energy Forecasting," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    3. Pei Du & Jianzhou Wang & Wendong Yang & Tong Niu, 2022. "A novel hybrid fine particulate matter (PM2.5) forecasting and its further application system: Case studies in China," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 64-85, January.
    4. Ying Wang & Jianzhou Wang & Hongmin Li & Hufang Yang & Zhiwu Li, 2022. "Multi‐step air quality index forecasting via data preprocessing, sequence reconstruction, and improved multi‐objective optimization algorithm," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1483-1511, November.
    5. Wang, Xiaolei & Xie, Naiming & Yang, Lu, 2022. "A flexible grey Fourier model based on integral matching for forecasting seasonal PM2.5 time series," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    6. Rameshwar Garg & Shriya Barpanda & Girish Rao Salanke N S & Ramya S, 2022. "Machine Learning Algorithms for Time Series Analysis and Forecasting," Papers 2211.14387, arXiv.org.
    7. Junbeom Park & Seongju Chang, 2021. "A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network," IJERPH, MDPI, vol. 18(13), pages 1-15, June.

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