Huang Liang, Ni Xuan, Ditto William L, Spano Mark, Carney Paul R, Lai Ying-Cheng
School of Physical Science and Technology , Lanzhou University , Lanzhou , Gansu 730000 , People's Republic of China.
School of Electrical , Computer and Energy Engineering , Arizona State University , Tempe , AZ 85287 , USA.
R Soc Open Sci. 2017 Jan 18;4(1):160741. doi: 10.1098/rsos.160741. eCollection 2017 Jan.
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. As a case study, we apply our framework to detecting and characterizing high-frequency oscillations (HFOs) from a big database of rat electroencephalogram recordings. We find a striking phenomenon: HFOs exhibit on-off intermittency that can be quantified by algebraic scaling laws. Our framework can be generalized to big data-related problems in other fields such as large-scale sensor data and seismic data analysis.
我们开发了一个框架，用于从海量、非线性和非平稳时间序列数据中发现并分析动态异常。该框架由三个步骤组成：对海量数据集进行预处理以消除错误数据段；应用经验模态分解和希尔伯特变换范式，以获取不同时间尺度下时间序列中嵌入的基本成分；以及对这些成分进行统计/标度分析。作为一个案例研究，我们将我们的框架应用于从大鼠脑电图记录的大型数据库中检测和表征高频振荡（HFOs）。我们发现了一个惊人的现象：HFOs表现出开-关间歇性，这种间歇性可以通过代数标度律进行量化。我们的框架可以推广到其他领域中与大数据相关的问题，如大规模传感器数据分析和地震数据分析。
