IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v57y2023i3d10.1007_s11135-022-01484-9.html
   My bibliography  Save this article

Cyclic clustering approach to impute missing values for cyclostationary hydrological time series

Author

Listed:
  • Mohammad Reza Mahmoudi

    (Fasa University)

Abstract

In different scientific fields, some parts of the collected dataset may include incomplete data with at least one missing value. To tackle this issue, statisticians and machine learning experts have introduced different missing value imputation (MVI) approaches. Hydrological data are mostly time series with stationary (or cyclic) trends that its observations have high autocorrelation (or cyclic auto-correlation). In addition, these time series frequently exhibit random and non-constant oscillations from their trends. Therefore, the selected MVI approach for hydrological time series should consider these serious aspects. One aspect of hydrological time series data that has not been closely examined in the MVI studies up to now is the existence of cyclic trend, named cyclostationarity. In this paper, a novel MVI approach for cyclostationary hydrological time series, called cyclic clustering, will be discussed. The performance of the proposed method is studied by employing numerous simulated datasets. The results indicate that the cyclic clustering approach is robust to impute missing values for both stationary and cyclostationary hydrological time series. The applicability of the introduced approach is also investigated in a real-world problem.

Suggested Citation

  • Mohammad Reza Mahmoudi, 2023. "Cyclic clustering approach to impute missing values for cyclostationary hydrological time series," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2619-2639, June.
    • Handle: RePEc:spr:qualqt:v:57:y:2023:i:3:d:10.1007_s11135-022-01484-9
    DOI: 10.1007/s11135-022-01484-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-022-01484-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11135-022-01484-9?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. Harry L. Hurd & Neil L. Gerr, 1991. "Graphical Methods For Determining The Presence Of Periodic Correlation," Journal of Time Series Analysis, Wiley Blackwell, vol. 12(4), pages 337-350, July.
    2. Mohammad Reza Mahmoudi & Mohsen Maleki, 2017. "A new method to detect periodically correlated structure," Computational Statistics, Springer, vol. 32(4), pages 1569-1581, December.
    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. Mahmoudi, Mohammad Reza & Heydari, Mohammad Hossein & Roohi, Reza, 2019. "A new method to compare the spectral densities of two independent periodically correlated time series," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 160(C), pages 103-110.
    2. Soumya Das & Marc G. Genton & Yasser M. Alshehri & Georgiy L. Stenchikov, 2021. "A cyclostationary model for temporal forecasting and simulation of solar global horizontal irradiance," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    3. Łukasz Lenart, 2017. "Examination of Seasonal Volatility in HICP for Baltic Region Countries: Non-Parametric Test versus Forecasting Experiment," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 9(1), pages 29-67, March.
    4. Mohammadi, M. & Rezakhah, S. & Modarresi, N., 2020. "Semi-Lévy driven continuous-time GARCH process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    5. Jentsch, Carsten & Subba Rao, Suhasini, 2015. "A test for second order stationarity of a multivariate time series," Journal of Econometrics, Elsevier, vol. 185(1), pages 124-161.
    6. Maleki, Mohsen & Mahmoudi, Mohammad Reza & Heydari, Mohammad Hossein & Pho, Kim-Hung, 2020. "Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    7. A. R. Soltani & A. R. Nematollahi & M. R. Mahmoudi, 2019. "On the asymptotic distribution of the periodograms for the discrete time harmonizable simple processes," Statistical Inference for Stochastic Processes, Springer, vol. 22(2), pages 307-322, July.
    8. Abdol Rassoul Zarei & Mohammad Reza Mahmoudi, 2020. "Ability Assessment of the Stationary and Cyclostationary Time Series Models to Predict Drought Indices," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 5009-5029, December.
    9. Lukasz Lenart, 2015. "Discrete Spectral Analysis. The Case of Industrial Production in Selected European Countries," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 15, pages 27-47.
    10. A. R. Nematollahi & A. R. Soltani & M. R. Mahmoudi, 2017. "Periodically correlated modeling by means of the periodograms asymptotic distributions," Statistical Papers, Springer, vol. 58(4), pages 1267-1278, December.
    11. Lenart, Łukasz, 2013. "Non-parametric frequency identification and estimation in mean function for almost periodically correlated time series," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 252-269.
    12. Mohammad Reza Mahmoudi & Abdol Rassoul Zarei, 2022. "Using Periodic Copula to Assess the Relationship Between Two Meteorological Cyclostationary Time Series Datasets," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4363-4388, September.
    13. Mahmoudi, Mohammad Reza & Baleanu, Dumitru & Mansor, Zulkefli & Tuan, Bui Anh & Pho, Kim-Hung, 2020. "Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    14. Mohammad Reza Mahmoudi & Mohsen Maleki, 2017. "A new method to detect periodically correlated structure," Computational Statistics, Springer, vol. 32(4), pages 1569-1581, December.
    15. Jeremy Penzer & Yorghos Tripodis, 2007. "Single-season heteroscedasticity in time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(3), pages 189-202.
    16. Sarnaglia, A.J.Q. & Reisen, V.A. & Lévy-Leduc, C., 2010. "Robust estimation of periodic autoregressive processes in the presence of additive outliers," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2168-2183, October.
    17. Baek, Changryong & Davis, Richard A. & Pipiras, Vladas, 2017. "Sparse seasonal and periodic vector autoregressive modeling," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 103-126.

    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:spr:qualqt:v:57:y:2023:i:3:d:10.1007_s11135-022-01484-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.