Predicting hourly ozone concentrations using wavelets and ARIMA models

被引:19
|
作者
Salazar, Ledys [1 ]
Nicolis, Orietta [1 ]
Ruggeri, Fabrizio [2 ]
Kisel'ak, Jozef [3 ,4 ,5 ]
Stehlik, Milan [1 ,4 ,5 ]
机构
[1] Univ Valparaiso, Inst Stat, Av Gran Bretana 1111, Valparaiso, Chile
[2] CNR, IMATI, Via Alfonso Corti 12, I-20133 Milan, Italy
[3] PJ Safarik Univ Kosice, Fac Sci, Inst Math, Kosice, Slovakia
[4] Johannes Kepler Univ Linz, Inst Appl Stat, Linz, Austria
[5] Johannes Kepler Univ Linz, Linz Inst Technol, Linz, Austria
来源
NEURAL COMPUTING & APPLICATIONS | 2019年 / 31卷 / 08期
关键词
Ozone (O-3); Discrete wavelet transform (DWT); Haar wavelet; Autoregressive integrated moving average (ARIMA); ARTIFICIAL NEURAL-NETWORKS; DECOMPOSITION;
D O I
10.1007/s00521-018-3345-0
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In recent years, air pollution has been a major concern for its implications on human health. Specifically, ozone (O-3) pollution is causing common respiratory diseases. In this paper, we illustrate the process of modeling and prediction hourly O-3 pollution measurements using wavelet transforms. We split the time series of O-3 in daily intervals and estimate scale and wavelet coefficients for each interval by the discrete wavelet transform (DWT) with Haar filter. Subsequently we apply cumulated autoregressive integrated moving average (ARIMA) to estimate the coefficients and forecast their evolution in future intervals. Then the inverse discrete wavelet transform is implemented for the reconstruction of the time series and the forecast in the near future. In order to assess the performance of the proposed methodology, we compare the predictions obtained by the DWT-ARIMA with those obtained by the ARIMA model. Several theoretical results are shown through a simulation study.
引用
收藏
页码:4331 / 4340
页数:10
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