Statistical Learning of Discrete States in Time Series
Statistical Learning of Discrete States in Time Series
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DOI：
10.1021/acs.jpcb.8b10561
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发表时间：
2019-01-24
影响因子：
3.3
通讯作者：
Yang, Haw
中科院分区：
文献类型：
--
作者：
Li, Hao;Yang, Haw
从依赖时间的实验获得的时间序列包含有关所研究系统的动力学和动力学的丰富信息。这项工作描述了一个无监督学习框架，以及必要的分析表达式的推导，用于分析呈现离散状态的高斯分布时间序列。使用先前开发的变化点（CP）方法以无模型方式将时间序列划分为片段后，该协议从凝聚层次聚类算法开始，将检测到的片段分类为可能的状态。使用期望最大化（EM）过程进一步细化初始状态聚类，并且状态数量由贝叶斯信息准则（BIC）确定。这里还介绍了人工智能文献中常见的成就标量化函数，用于定量评估状态确定的性能。统计学习框架由三个阶段组成：信号变化检测、聚类和状态数确定，使用具有无基础动力学的随机强度段的模拟轨迹来彻底表征，并严格评估其性能。还演示了对实验数据的应用。结果表明，这个总体框架的实施基于坚实的理论基础，不需要强加任何动力学模型，在确定状态数量、每个状态中包含的参数以及相关的统计显着性方面具有强大的作用。
Time series obtained from time-dependent experiments contain rich information on kinetics and dynamics of the system under investigation. This work describes an unsupervised learning framework, along with the derivation of the necessary analytical expressions, for the analysis of Gaussian-distributed time series that exhibit discrete states. After the time series has been partitioned into segments in a model-free manner using the previously developed change-point (CP) method, this protocol starts with an agglomerative hierarchical clustering algorithm to classify the detected segments into possible states. The initial state clustering is further refined using an expectation-maximization (EM) procedure, and the number of states is determined by a Bayesian information criterion (BIC). Also introduced here is an achievement scalarization function, usually seen in artificial intelligence literature, for quantitatively assessing the performance of state determination. The statistical learning framework, which is comprised of three stages, detection of signal change, clustering, and number-of-state determination, was thoroughly characterized using simulated trajectories with random intensity segments that have no underlying kinetics, and its performance was critically evaluated. The application to experimental data is also demonstrated. The results suggested that this general framework, the implementation of which is based on firm theoretical foundations and does not require the imposition of any kinetics model, is powerful in determining the number of states, the parameters contained in each state, as well as the associated statistical significance.