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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- Digital object identifier: 10.1098/rsos.160741
- Identifier TypeInternational standard serial numberIdentifier Value2054-5703
- The final version of this article, as published in Royal Society Open Science, can be viewed online at: http://rsos.royalsocietypublishing.org/content/4/1/160741, opens in a new window
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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