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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Roberts, S. & Nowak, G., 2014. "Stabilizing the lasso against cross-validation variability," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 198-211.
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    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.

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