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

Ping Wang, Hongyinping Feng, Guisheng Zhang and Daizong Yu
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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

Sustainability, 2020, vol. 12, issue 11, 1-15

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.

Keywords: air quality index; time series forecasting; Luenberger observer; period-aware hybrid model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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