IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v172y2023ics0960077923004599.html
   My bibliography  Save this article

Detrending moving-average cross-correlation based principal component analysis of air pollutant time series

Author

Listed:
  • Dong, Xiaofeng
  • Fan, Qingju
  • Li, Dan

Abstract

This work investigates the principal component of air pollutants. The approach is based on detrending moving-average cross-correlation analysis(DMCA) and principal component analysis (PCA). We illustrate the advantages of this method by performing several comparative numerical analysis with traditional principal component analysis (PCA). The results indicate that the principal components obtained by DMCA-based PCA are more reliable in small and medium scale range, and the new method is relatively immune to additive trend and non-stationarity. To further show the utility of DMCA-based PCA in natural complex systems, six air pollutants data collected in Beijing from December 2013 to November 2016 are investigated seasonally. We found that the pollutants PM2.5, PM10 and CO are the most important factors affecting air quality of Beijing, and O3 is the secondary contaminants among four seasons. The contributors to the principal components in winter are the most stable for all time scales, and the second are that in autumn. With these physically explainable results, we have confidence that DMCA-based PCA is an useful method in addressing non-stationary signals.

Suggested Citation

  • Dong, Xiaofeng & Fan, Qingju & Li, Dan, 2023. "Detrending moving-average cross-correlation based principal component analysis of air pollutant time series," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
  • Handle: RePEc:eee:chsofr:v:172:y:2023:i:c:s0960077923004599
    DOI: 10.1016/j.chaos.2023.113558
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077923004599
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2023.113558?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ying-Hui Shao & Gao-Feng Gu & Zhi-Qiang Jiang & Wei-Xing Zhou, 2015. "Effects of polynomial trends on detrending moving average analysis," Papers 1505.02750, arXiv.org.
    2. Yue-Hua Dai & Wei-Xing Zhou, 2017. "Temporal and spatial correlation patterns of air pollutants in Chinese cities," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-24, August.
    3. Amato, Federico & Laib, Mohamed & Guignard, Fabian & Kanevski, Mikhail, 2020. "Analysis of air pollution time series using complexity-invariant distance and information measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    4. He, Ling-Yun & Chen, Shu-Peng, 2011. "A new approach to quantify power-law cross-correlation and its application to commodity markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3806-3814.
    5. Shen, Chenhua, 2017. "A comparison of principal components using TPCA and nonstationary principal component analysis on daily air-pollutant concentration series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 453-464.
    6. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    7. Zebende, G.F., 2011. "DCCA cross-correlation coefficient: Quantifying level of cross-correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 614-618.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kakinaka, Shinji & Umeno, Ken, 2021. "Exploring asymmetric multifractal cross-correlations of price–volatility and asymmetric volatility dynamics in cryptocurrency markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    2. Ladislav Kristoufek & Paulo Ferreira, 2018. "Capital asset pricing model in Portugal: Evidence from fractal regressions," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 17(3), pages 173-183, November.
    3. Zhai, Lu-Sheng & Liu, Ruo-Yu, 2019. "Local detrended cross-correlation analysis for non-stationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 222-233.
    4. Ferreira, Paulo & Kristoufek, Ladislav, 2017. "What is new about covered interest parity condition in the European Union? Evidence from fractal cross-correlation regressions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 554-566.
    5. Paulo Ferreira & Marcus Fernandes da Silva & Idaraí Santos de Santana, 2019. "Detrended Correlation Coefficients Between Exchange Rate (in Dollars) and Stock Markets in the World’s Largest Economies," Economies, MDPI, vol. 7(1), pages 1-11, February.
    6. Kristoufek, Ladislav, 2018. "Fractality in market risk structure: Dow Jones Industrial components case," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 69-75.
    7. repec:arx:papers:1501.02947 is not listed on IDEAS
    8. Kristoufek, Ladislav, 2015. "Power-law correlations in finance-related Google searches, and their cross-correlations with volatility and traded volume: Evidence from the Dow Jones Industrial components," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 194-205.
    9. Paulo Ferreira, 2017. "Portuguese and Brazilian stock market integration: a non-linear and detrended approach," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 16(1), pages 49-63, April.
    10. Zebende, G.F. & da Silva, M.F. & Machado Filho, A., 2013. "DCCA cross-correlation coefficient differentiation: Theoretical and practical approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(8), pages 1756-1761.
    11. Fernández-Martínez, M. & Sánchez-Granero, M.A. & Casado Belmonte, M.P. & Trinidad Segovia, J.E., 2020. "A note on power-law cross-correlated processes," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    12. Cao, Guangxi & Xu, Longbing & Cao, Jie, 2012. "Multifractal detrended cross-correlations between the Chinese exchange market and stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4855-4866.
    13. Kristoufek, Ladislav, 2014. "Leverage effect in energy futures," Energy Economics, Elsevier, vol. 45(C), pages 1-9.
    14. Anderson Palmeira & Éder Pereira & Paulo Ferreira & Luisa Maria Diele-Viegas & Davidson Martins Moreira, 2022. "Long-Term Correlations and Cross-Correlations in Meteorological Variables and Air Pollution in a Coastal Urban Region," Sustainability, MDPI, vol. 14(21), pages 1-12, November.
    15. Wang, Gang-Jin & Xie, Chi & He, Ling-Yun & Chen, Shou, 2014. "Detrended minimum-variance hedge ratio: A new method for hedge ratio at different time scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 70-79.
    16. Shen, Chenhua, 2017. "A comparison of principal components using TPCA and nonstationary principal component analysis on daily air-pollutant concentration series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 453-464.
    17. Kristoufek, Ladislav, 2014. "Detrending moving-average cross-correlation coefficient: Measuring cross-correlations between non-stationary series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 169-175.
    18. El Alaoui, Marwane, 2015. "Random matrix theory and portfolio optimization in Moroccan stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 92-99.
    19. Kristoufek, Ladislav, 2015. "Finite sample properties of power-law cross-correlations estimators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 513-525.
    20. Kristoufek, Ladislav, 2014. "Measuring correlations between non-stationary series with DCCA coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 291-298.
    21. Paiva, Aureliano Sancho Souza & Rivera-Castro, Miguel Angel & Andrade, Roberto Fernandes Silva, 2018. "DCCA analysis of renewable and conventional energy prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1408-1414.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:172:y:2023:i:c:s0960077923004599. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.