Description

We develop a framework to uncover and analyse dynamical anomalies from massive, nonlinear and non-stationary time series data. The framework consists of three steps: preprocessing of massive datasets to eliminate

We develop a framework to uncover and analyse dynamical anomalies from massive, nonlinear and non-stationary time series data. The framework consists of three steps: preprocessing of massive datasets to eliminate erroneous data segments, application of the empirical mode decomposition and Hilbert transform paradigm to obtain the fundamental components embedded in the time series at distinct time scales, and statistical/scaling analysis of the components.

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    Date Created
    • 2017-01-18
    Resource Type
  • Text
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    Identifier
    • Digital object identifier: 10.1098/rsos.160741
    • Identifier Type
      International standard serial number
      Identifier Value
      2054-5703
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    Huang, L., Ni, X., Ditto, W. L., Spano, M., Carney, P. R., & Lai, Y. (2017). Detecting and characterizing high-frequency oscillations in epilepsy: a case study of big data analysis. Royal Society Open Science, 4(1), 160741. doi:10.1098/rsos.160741

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