IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v127y2018icp204-216.html
   My bibliography  Save this article

Classification tree methods for panel data using wavelet-transformed time series

Author

Listed:
  • Zhao, Xin
  • Barber, Stuart
  • Taylor, Charles C.
  • Milan, Zoka

Abstract

Wavelet-transformed variables can have better classification performance for panel data than using variables on their original scale. Examples are provided showing the types of data where using a wavelet-based representation is likely to improve classification accuracy. Results show that in most cases wavelet-transformed data have better or similar classification accuracy to the original data, and only select genuinely useful explanatory variables. Use of wavelet-transformed data provides localized mean and difference variables which can be more effective than the original variables, provide a means of separating “signal” from “noise”, and bring the opportunity for improved interpretation via the consideration of which resolution scales are the most informative. Panel data with multiple observations on each individual require some form of aggregation to classify at the individual level. Three different aggregation schemes are presented and compared using simulated data and real data gathered during liver transplantation. Methods based on aggregating individual level data before classification outperform methods which rely solely on the combining of time-point classifications.

Suggested Citation

  • Zhao, Xin & Barber, Stuart & Taylor, Charles C. & Milan, Zoka, 2018. "Classification tree methods for panel data using wavelet-transformed time series," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 204-216.
    • Handle: RePEc:eee:csdana:v:127:y:2018:i:c:p:204-216
    DOI: 10.1016/j.csda.2018.05.019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947318301348
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2018.05.019?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. Maharaj, Elizabeth Ann & Alonso, Andrés M., 2014. "Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 67-87.
    2. Roberts, S. & Nowak, G., 2014. "Stabilizing the lasso against cross-validation variability," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 198-211.
    3. Maharaj, Elizabeth A. & Alonso, Andres M., 2007. "Discrimination of locally stationary time series using wavelets," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 879-895, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Huifang Sun & Yaoguo Dang & Wenxin Mao, 2019. "Identifying key factors of regional agricultural drought vulnerability using a panel data grey combined method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 98(2), pages 621-642, September.
    2. Xin Zhao & Stuart Barber & Charles C. Taylor & Zoka Milan, 2021. "Interval forecasts based on regression trees for streaming data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(1), pages 5-36, March.

    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. Carolina Euán & Hernando Ombao & Joaquín Ortega, 2018. "The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 71-99, April.
    2. Aykroyd, Robert G. & Barber, Stuart & Miller, Luke R., 2016. "Classification of multiple time signals using localized frequency characteristics applied to industrial process monitoring," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 351-362.
    3. Hossein Hassani & Mohammad Reza Yeganegi & Emmanuel Sirimal Silva, 2018. "A New Signal Processing Approach for Discrimination of EEG Recordings," Stats, MDPI, vol. 1(1), pages 1-14, November.
    4. Liu, Shen & Maharaj, Elizabeth Ann & Inder, Brett, 2014. "Polarization of forecast densities: A new approach to time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 345-361.
    5. La Vecchia, Davide & Camponovo, Lorenzo & Ferrari, Davide, 2015. "Robust heart rate variability analysis by generalized entropy minimization," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 137-151.
    6. Ángel López-Oriona & José A. Vilar, 2021. "F4: An All-Purpose Tool for Multivariate Time Series Classification," Mathematics, MDPI, vol. 9(23), pages 1-26, November.
    7. Elizabeth Ann Maharaj & Pierpaolo D’Urso & Don Galagedera, 2010. "Wavelet-based Fuzzy Clustering of Time Series," Journal of Classification, Springer;The Classification Society, vol. 27(2), pages 231-275, September.
    8. Maharaj, Elizabeth Ann, 2012. "Discriminant analysis of multivariate time series using wavelets," DES - Working Papers. Statistics and Econometrics. WS ws120603, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Andrés Alonso & David Casado & Sara López-Pintado & Juan Romo, 2014. "Robust Functional Supervised Classification for Time Series," Journal of Classification, Springer;The Classification Society, vol. 31(3), pages 325-350, October.
    10. Liu, Shen & Maharaj, Elizabeth Ann, 2013. "A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 32-49.
    11. Mohamed Ouhourane & Yi Yang & Andréa L. Benedet & Karim Oualkacha, 2022. "Group penalized quantile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 495-529, September.
    12. Casado, David & López Pintado, Sara, 2008. "A functional data based method for time series classification," DES - Working Papers. Statistics and Econometrics. WS ws087427, Universidad Carlos III de Madrid. Departamento de Estadística.
    13. Pedro Delicado & Philippe Vieu, 2017. "Choosing the most relevant level sets for depicting a sample of densities," Computational Statistics, Springer, vol. 32(3), pages 1083-1113, September.
    14. Embleton, Jonathan & Knight, Marina I. & Ombao, Hernando, 2022. "Wavelet testing for a replicate-effect within an ordered multiple-trial experiment," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    15. Emma Saulnier & Olivier Gascuel & Samuel Alizon, 2017. "Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study," PLOS Computational Biology, Public Library of Science, vol. 13(3), pages 1-31, March.
    16. Maharaj, Elizabeth Ann & Alonso, Andrés M., 2014. "Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 67-87.
    17. Leo Huang & Kuang-Yu Chang & Yu-Chen Yeh, 2020. "How Can Travel Agencies Create Sustainable Competitive Advantages? Perspective on Employee Role Stress and Initiative Behavior," Sustainability, MDPI, vol. 12(11), pages 1-15, June.

    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:csdana:v:127:y:2018:i:c:p:204-216. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.