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DOI	10.1016/j.atmosenv.2020.117755
	Daily PM10, periodicity and harmonic regression model: The case of London
	Okkaoğlu Y.; Akdi Y.; Ünlü K.D.
发表日期	2020
ISSN	13522310
卷号	238
英文摘要	One of the most important and distinguishable features of the climate driven data can be shown as the seasonality. Due to its nature air pollution data may have hourly, daily, weekly, monthly or even seasonal cycles. Many techniques such as non-linear time series analysis, machine learning algorithms and deterministic models, have been used to deal with this non-linear structure. Although, these models can capture the seasonality they can't identify the periodicity. Periodicity is beyond the seasonality, it is the hidden pattern of the time series. In this study, it is aimed to investigate the periodicity of daily Particulate Matter (PM10) of London between the periods 2014 and 2018. PM10 is the particulate matter of which aerodynamic diameter is less than 10 μm. Firstly, periodogram based unit root test is used to check the stationarity of the investigated data. Afterwards, hidden periodic structure of the data is revealed. It is found that, it has five different cycle periods as 7 days, 25 days, 6 months, a year and 15 months. Lastly, it is shown that harmonic regression performs better in forecasting monthly and daily averages of the data. © 2020 Elsevier Ltd
英文关键词	Air pollution; Harmonic regression; London; Nonlinear time series analysis; Periodograms; PM10
语种	英语
scopus关键词	Learning algorithms; Machine learning; Particles (particulate matter); Regression analysis; Aerodynamic diameters; Deterministic models; Harmonic regression; Hidden patterns; Nonlinear structure; Nonlinear time-series analysis; Particulate Matter; Unit root tests; Time series analysis; algorithm; atmospheric pollution; harmonic analysis; machine learning; particulate matter; periodicity; regression analysis; seasonal variation; seasonality; air pollution; article; England; forecasting; particulate matter; periodicity; seasonal variation; time series analysis; London
来源期刊	Atmospheric Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/153009
作者单位	University of Southampton, Department of Mathematical Sciences, Southampton, SO17 1BJ, United Kingdom; Ankara University, Department of Statistics, Ankara, 06100, Turkey; Atilim University, Department of Mathematics, Ankara, 06830, Turkey
推荐引用方式 GB/T 7714	Okkaoğlu Y.,Akdi Y.,Ünlü K.D.. Daily PM10, periodicity and harmonic regression model: The case of London[J],2020,238.
APA	Okkaoğlu Y.,Akdi Y.,&Ünlü K.D..(2020).Daily PM10, periodicity and harmonic regression model: The case of London.Atmospheric Environment,238.
MLA	Okkaoğlu Y.,et al."Daily PM10, periodicity and harmonic regression model: The case of London".Atmospheric Environment 238(2020).