Fast change point detection in switching dynamics using a hidden Markov model of prediction experts
Fast change point detection in switching dynamics using a hidden Markov model of prediction experts
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DOI：
10.1049/cp:19991109
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
J. Kohlmorgen;S. Lemm;K. Müller;S. Liehr;K. Pawelzik
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文献类型：
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作者：
J. Kohlmorgen;S. Lemm;K. Müller;S. Liehr;K. Pawelzik
我们提出了一个框架，从时间序列，允许一个快速的动态模式变化的在线检测切换动态建模。该方法是基于隐马尔可夫模型（HMM）的预测专家。通过期望最大化（EM）和使用HMM状态概率的退火时间表来训练预测器。这导致将时间序列分割成不同的动态模式，并同时专业化预测专家的部分。在第二步骤中，为每个专家生成输入密度估计器。它可以简单地从分配给相应专家的数据子集计算。结合HMM状态概率，这允许非常快速地在线检测模式变化：只要传入的输入数据流包含足够的信息来指示动态变化，就可以检测到变化点。
We present a framework for modeling switching dynamics from a time series that allows for a fast online detection of dynamical mode changes. The method is based on a hidden Markov model (HMM) of prediction experts. The predictors are trained by expectation maximization (EM) and by using an annealing schedule for the HMM state probabilities. This leads to a segmentation of the time series into different dynamical modes and a simultaneous specialization of the prediction experts on the segments. In a second step, an input-density estimator is generated for each expert. It can simply be computed from the data subset assigned to the respective expert. In conjunction with the HMM state probabilities, this allows for a very fast online detection of mode changes: change points are detected as soon as the incoming input data stream contains sufficient information to indicate a change in the dynamics.