Daily PM10, periodicity and harmonic regression model: The case of London

Okkaoğlu, Yasin; Akdi, Yılmaz; Ünlü, Kamil Demirberk

doi:10.1016/j.atmosenv.2020.117755

Cited by 17 publications

(5 citation statements)

References 32 publications

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“…In the methodology we will follow the procedure given in [3], which uses periodogram based time series analysis to identify the cycles in the monthly weather pollution data of Ankara, Turkey. In recent years, periodogram based time series analysis and periodogram based unit root test has many applications in energy [1] weather pollution [21] and finance and economics [5]. It is very useful and powerful modeling framework since it requires no assumption or parameter estimations except for the variance of the white noise series.…”

Section: Methodsmentioning

confidence: 99%

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Investigating the periodicities of the Turkish food inflation with considering the subgroups

Ünlü

Akdi

Baş³

et al. 2022

Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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This paper empirically identifies the hidden periodicities in the time series of monthly food inflation of Turkey with its subgroups for the periods from January, 2004 to June, 2020. Periodicity is the hidden cycles of the time series which is beyond the seasonality. In order to deal with this hidden cycles periodogram based time series analysis is used. Also, future inflation ratios are forecasted by Harmonic regression. The results of this study reveal that food inflation of Turkey has cycles of approximately 2 years which is mostly derived by food and non-alcoholic beverage inflation.

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Section: Methodsmentioning

confidence: 99%

“…It is very useful and powerful modeling framework since it requires no assumption or parameter estimations except for the variance of the white noise series. In this section we will summarize the methodology more details about it can be found in [21].…”

Section: Methodsmentioning

confidence: 99%

Investigating the periodicities of the Turkish food inflation with considering the subgroups

Ünlü

Akdi

Baş³

et al. 2022

Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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“…In this study, we use the methodology proposed by Akdi et al (2020a) which use periodogrambased time series to model monthly air pollution of Ankara, the capital city of Turkey. The recent literature shows that periodogram-based time series has many applications in economics Akdi et al (2020b); in weather pollutions Okkaoğlu et al (2020); in energy markets Akdi et al (2020c); and in meteorology Akdi and Ünlü (2020). The methodology has many advantages, but the most significant one can be shown that it does not require any assumption or parameter estimations except for the variance of the white noise series.…”

Section: Methodsmentioning

confidence: 99%

“…Also, it is shown that harmonic regression models with trigonometric components outperform the classical ARIMA type model. Okkaoğlu et al (2020) use periodogram-based time series analysis to enlighten the hidden periodicities of the daily PM 10 data of London between the periods of 2014 and 2018. Their results indicate that London's data has 7-day, 25-day, 6-month, 1-year, and 15-month periodicities.…”

Section: Introductionmentioning

confidence: 99%

Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology

Akdi

Gölveren

Ünlü

et al. 2021

Environ Monit Assess

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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.

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“…One study accounted seasonal non-stationarity in time series models for short-term ozone level forecasts [16]; and simulation of the daily average PM10 concentrations were done at Ta-Liao [17], while another used ARIMA to forecast a full range of pollutants-ozone (O 3 ), nitrogen oxide (NO), nitrogen dioxide (NO 2 ) and carbon dioxide (CO) [18]. Hidden periodicities of the fine particle (PM10) time series were identified and used to increase the performance of the time series models [19]. The duration of cycle is calculated for observed PM10 levels in London as 365 days corresponding to a year, 7 days corresponding to a weak, 456 days corresponding to 15 months, and 183 days corresponding to 6 months and 25 days.…”

Section: Introductionmentioning

confidence: 99%

Time Series Forecasting of Air Quality: A Case Study of Sofia City

2022

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Air pollution has a significant impact on human health and the environment, causing cardiovascular disease, respiratory infections, lung cancer and other diseases. Understanding the behavior of air pollutants is essential for adequate decisions that can lead to a better quality of life for citizens. Air quality forecasting is a reliable method for taking preventive and regulatory actions. Time series analysis produces forecasting models, which study the characteristics of the data points over time to extrapolate them in the future. This study explores the trends of air pollution at five air quality stations in Sofia, Bulgaria. The data collected between 2015 and 2019 is analyzed applying time series forecasting. Since the time series analysis works on complete data, imputation techniques are used to deal with missing values of pollutants. The data is aggregated by granularity periods of 3 h, 6 h, 12 h, 24 h (1 day). The AutoRegressive Integrated Moving Average (ARIMA) method is employed to create statistical analysis models for the prediction of pollutants’ levels at each air quality station and for each granularity, including carbon oxide (CO), nitrogen dioxide (NO2), ozone (O3) and fine particles (PM2.5). In addition, the method allows us to find out whether the pollutants’ levels exceed the limits prescribed by the World Health Organization (WHO), as well as to investigate the correlation between levels of a given pollutant measured in different air quality stations.

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Daily PM10, periodicity and harmonic regression model: The case of London

Cited by 17 publications

References 32 publications

Investigating the periodicities of the Turkish food inflation with considering the subgroups

Investigating the periodicities of the Turkish food inflation with considering the subgroups

Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology

Time Series Forecasting of Air Quality: A Case Study of Sofia City

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