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A Period-Aware Hybrid Model Applied for Forecasting AQI Time Series

Author

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  • Ping Wang

    (College of Resources and Environment, Shanxi University of Finance and Economics, Taiyuan 030006, China)

  • Hongyinping Feng

    (School of Mathematical Sciences, Shanxi University, Taiyuan 030006, China)

  • Guisheng Zhang

    (School of Economics and Management, Shanxi University, Taiyuan 030006, China)

  • Daizong Yu

    (College of Resources and Environment, Shanxi University of Finance and Economics, Taiyuan 030006, China)

Abstract

An accurate, reliable and stable air quality prediction system is conducive to the public health and management of atmospheric ecological environment; therefore, many models, individual or hybrid, have been implemented widely to deal with the prediction problem. However, many of these models do not take into consideration or extract improperly the period information in air quality index (AQI) time series, which impacts the models’ learning efficiency greatly. In this paper, a period extraction algorithm is proposed by using a Luenberger observer, and then a novel period-aware hybrid model combined the period extraction algorithm and tradition time series models is build to exploit the comprehensive forecasting capacity to the AQI time series with nonlinear and non-stationary noise. The hybrid model requires a multi-phase implementation. In the first step, the Luenberger observer is used to estimate the implied period function in the one-dimensional AQI series, and then the analyzed time series is mapped to the period space through the function to obtain the period information sub-series of the original series. In the second step, the period sub-series is combined with the original input vector as input vector components according to the time points to establish a new data set. Finally, the new data set containing period information is applied to train the traditional time series prediction models. Both theoretical proof and experimental results obtained on the AQI hour values of Beijing, Tianjin, Taiyuan and Shijiazhuang in North China prove that the hybrid model with period information presents stronger robustness and better forecasting accuracy than the traditional benchmark models.

Suggested Citation

  • Ping Wang & Hongyinping Feng & Guisheng Zhang & Daizong Yu, 2020. "A Period-Aware Hybrid Model Applied for Forecasting AQI Time Series," Sustainability, MDPI, vol. 12(11), pages 1-15, June.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:11:p:4730-:d:369416
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    References listed on IDEAS

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    1. Wang, Jianzhou & Hu, Jianming, 2015. "A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vec," Energy, Elsevier, vol. 93(P1), pages 41-56.
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    Cited by:

    1. Md. Arif Hossen & Israt Jahan Ruva & Md. Mehedi Hassan Masum & Prabal Barua, 2022. "Status Of Air Quality And Noise Level With Associated Health Risk Vicinity To Shipbreaking Yards Of Bangladesh," Environment & Ecosystem Science (EES), Zibeline International Publishing, vol. 6(2), pages 83-93, September.
    2. Paulo S. G. de Mattos Neto & Manoel H. N. Marinho & Hugo Siqueira & Yara de Souza Tadano & Vivian Machado & Thiago Antonini Alves & João Fausto L. de Oliveira & Francisco Madeiro, 2020. "A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition," Sustainability, MDPI, vol. 12(18), pages 1-33, September.
    3. Petropoulos, Fotios & Makridakis, Spyros & Stylianou, Neophytos, 2022. "COVID-19: Forecasting confirmed cases and deaths with a simple time series model," International Journal of Forecasting, Elsevier, vol. 38(2), pages 439-452.

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