论文标题
开发用于识别时间序列系统动态变化的算法
Development of an Algorithm for Identifying Changes in System Dynamics from Time Series
论文作者
论文摘要
提出了具有相关数学概念的算法的开发，并支持假设，以检测时间序列的系统动力学变化以及经验分析和理论依据。对于该方法，将Markov链（SLEM）或混合速率的第二大特征值的变化视为系统动力学变化的指标。马尔可夫链是由时间序列的经验过渡概率创建的。该方法是用于应用麻醉猪中动脉血压出血的应用。通过人造血压模型对SLEM变化的理由进行了研究，并通过研究与其他措施（如时间序列平滑度）以及Markov链的过渡概率矩阵的密度相关性。数学分析表明，SLEM的变化是不同状态之间过渡概率变化的结果，并反映了有关系统动力学的信息。
The development of an algorithm with related mathematical concepts and supporting hypothesis for detecting changes in system dynamics from time series along with empirical analysis and theoretical justification is presented. For the method, changes in the second largest eigenvalue of Markov Chain (SLEM) or mixing rate, is observed as an indicator of the changes in system dynamics. The Markov chain is created from empirical transition probabilities of a time series. The method is developed for the application of detecting hemorrhage from arterial blood pressure in anesthetized swine. The rationale of the change in the SLEM is investigated empirically with an artificial blood pressure model and, by studying correlations with other measures such as smoothness of time series, and density of the transition probability matrix of the Markov chain. The mathematical analysis shows that the change in the SLEM is a consequence of the change in the transition probabilities between different states and reflects information about the system dynamics.
