Modeling and Forecasting of Monthly Pm<sub>2.5</Sub> Emission of Paris by Periodogram-Based Time Series Methodology

dc.authorid Ünlü, Kamil Demirberk/0000-0002-2393-6691
dc.authorid Yucel, Eray/0000-0002-1038-4357
dc.authorscopusid 9248321700
dc.authorscopusid 57212081476
dc.authorscopusid 57210105250
dc.authorscopusid 59157974600
dc.authorwosid Ünlü, Kamil Demirberk/AAL-5952-2020
dc.authorwosid Yucel, Eray/AAG-5262-2019
dc.contributor.author Akdi, Yilmaz
dc.contributor.author Golveren, Elif
dc.contributor.author Unlu, Kamil Demirberk
dc.contributor.author Yucel, Mustafa Eray
dc.contributor.other Industrial Engineering
dc.date.accessioned 2024-07-05T15:16:53Z
dc.date.available 2024-07-05T15:16:53Z
dc.date.issued 2021
dc.department Atılım University en_US
dc.department-temp [Akdi, Yilmaz] Ankara Univ, Dept Stat, Fac Sci, Ankara, Turkey; [Golveren, Elif] Ataturk Univ, Fac Econ & Adm Sci, Dept Econometr, Erzurum, Turkey; [Unlu, Kamil Demirberk] Atilim Univ, Dept Math, Ankara, Turkey; [Yucel, Mustafa Eray] Ihsan Dogramaci Bilkent Univ, Fac Econ Adm & Social Sci, Dept Econ, Ankara, Turkey en_US
dc.description Ünlü, Kamil Demirberk/0000-0002-2393-6691; Yucel, Eray/0000-0002-1038-4357 en_US
dc.description.abstract In this study, monthly particulate matter (PM2.5) of Paris for the period between January 2000 and December 2019 is investigated by utilizing a periodogram-based time series methodology. The main contribution of the study is modeling the PM2.5 of Paris by extracting the information purely from the examined time series data, where proposed model implicitly captures the effects of other factors, as all their periodic and seasonal effects reside in the air pollution data. Periodicity can be defined as the patterns embedded in the data other than seasonality, and it is crucial to understand the underlying periodic dynamics of air pollutants to better fight pollution. The method we use successfully captures and accounts for the periodicities, which could otherwise be mixed with seasonality under an alternative methodology. Upon the unit root test based on periodograms, it is revealed that the investigated data has periodicities of 1 year and 20 years, so harmonic regression is utilized as an alternative to Box-Jenkins methodology. As the harmonic regression displayed a better performance both in and out-of-sample forecasts, it can be considered as a powerful alternative to model and forecast time series with a periodic structure. en_US
dc.identifier.citationcount 13
dc.identifier.doi 10.1007/s10661-021-09399-y
dc.identifier.issn 0167-6369
dc.identifier.issn 1573-2959
dc.identifier.issue 10 en_US
dc.identifier.pmid 34477984
dc.identifier.scopus 2-s2.0-85114282547
dc.identifier.uri https://doi.org/10.1007/s10661-021-09399-y
dc.identifier.uri https://hdl.handle.net/20.500.14411/1687
dc.identifier.volume 193 en_US
dc.identifier.wos WOS:000692445100001
dc.identifier.wosquality Q3
dc.institutionauthor Ünlü, Kamil Demirberk
dc.language.iso en en_US
dc.publisher Springer en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 17
dc.subject Harmonic regression en_US
dc.subject Air pollution en_US
dc.subject Time series analysis en_US
dc.subject Periodicity en_US
dc.title Modeling and Forecasting of Monthly Pm<sub>2.5</Sub> Emission of Paris by Periodogram-Based Time Series Methodology en_US
dc.type Article en_US
dc.wos.citedbyCount 15
dspace.entity.type Publication
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