Finding Stationary Subspaces in Multivariate Time Series
Finding Stationary Subspaces in Multivariate Time Series
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DOI：
10.1103/physrevlett.103.214101
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发表时间：
2009-11-20
影响因子：
8.6
通讯作者：
Mueller, Klaus-Robert
中科院分区：
文献类型：
--
作者：
von Buenau, Paul;Meinecke, Frank C.;Mueller, Klaus-Robert
识别复杂多元时间序列中的时间不变分量是理解底层动力系统并预测其未来行为的关键。在这封信中，我们提出了一种新技术，即平稳子空间分析（SSA），它将多元时间序列分解为其平稳部分和非平稳部分。该方法基于两个假设：（a）观测到的信号是固定源和非固定源的线性叠加； (b) 非平稳性在前两个时刻是可测量的。我们描述了 SSA 的理论和实践特性，并在模拟和脑电图测量的皮层信号中对其进行了研究。在这里，SSA 成功地找到了固定组件，从而显着提高了预测准确性和有意义的地形图，从而有助于更好地理解潜在的非固定大脑过程。
Identifying temporally invariant components in complex multivariate time series is key to understanding the underlying dynamical system and predict its future behavior. In this Letter, we propose a novel technique, stationary subspace analysis (SSA), that decomposes a multivariate time series into its stationary and nonstationary part. The method is based on two assumptions: (a) the observed signals are linear superpositions of stationary and nonstationary sources; and (b) the nonstationarity is measurable in the first two moments. We characterize theoretical and practical properties of SSA and study it in simulations and cortical signals measured by electroencephalography. Here, SSA succeeds in finding stationary components that lead to a significantly improved prediction accuracy and meaningful topographic maps which contribute to a better understanding of the underlying nonstationary brain processes.