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

Ping Wang,Hongyinping Feng,Guisheng Zhang,Daizong Yu

doi:10.3390/su12114730

Ping Wang, Hongyinping Feng + Show 2 more

Open Access

https://doi.org/10.3390/su12114730

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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.

Highlights

Air quality index (AQI) with the dimensionless attribute reflects the air quality of a specific area quantitatively [1]
According to the displayed results, we can draw the conclusion that the AQI data are provided with the stationarity property, which means that the AQI time series meet the requirements of ARIMA modeling
The models proposed in this paper have smaller mean absolute error (MAE), root mean square error (RMSE) and larger index of agreement (IA) and direction accuracy (DA) in all cities, which means that these models with period information are superior to the corresponding benchmark models

Summary

Introduction

Air quality index (AQI) with the dimensionless attribute reflects the air quality of a specific area quantitatively [1]. Wang et al [4] proposed a hybrid model based on multiple intelligent algorithms integrating the decomposition technique with extreme learning machine (ELM), which acquitted itself of AQI forecast in Beijing and Shanghai splendidly. Jiang et al [5] used the improved pigeon-inspired optimization (IPIO) method to optimize the parameters of ELM, which was applied to assemble the subseries, and K-means clustering methods combined with multidimensional scaling clustered the prediction results. This method is applied to the different terms prediction of Harbin’s AQI, which showed better generalization ability than the benchmark models. Zhu et al [8] presented a novel optimal-combined AQI forecasting method to effectively avoid the uncertainty and instability brought by blind combined model

Methods

Results

Conclusion