基于隐含模式的异常检测算法
doi: 10.3724/SP.J.1146.2005.01392
An Anomaly Detection Algorithm Based on Hidden Pattern
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摘要: 如何检测系统中的临界变化,一直是一个难题。该文提供了一种新的基于隐含模式的异常检测算法。机是一种新的计算力学理论,它能从时间序列中发掘系统的隐含模式。因果态分割重建算法(CSSR)是目前重构机的最成熟算法,它可以推理出一个因果态集合,所有的因果态构成一个隐马尔可夫模型。在因果态集合的基础上,建立一个表达系统特征的向量,不同向量间的距离可以定义成系统异常的测度。把时间序列分段,分别计算每部分的异常度,就可以得到系统的异常演变曲线。在Duffing振子的例子中,该算法不仅有效检测,还提前预测到系统分叉的发生,说明该算法具有很好的应用潜力。
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关键词:
- 异常检测;隐含模式;机;时间序列
Abstract: It is a difficult problem how to detect such accident of a system. This paper presents a new algorithm, an anomaly detection algorithm based on hidden pattern. Epsilon machine, a new computational mechanics, can discover hidden pattern from the response time series. Causal State Splitting Reconstruction (CSSR), one algorithm of epsilon machine, can infer a set of causal states, which has an analogy to hidden Markov chain. Based on this set, an anomaly measure can be defined, which is the distance of two characteristic vectors. Computing all parts of the time series, an anomaly evolution curve can be got. In simulation analysis of Duffing equation, step changes appear in the anomaly curve, before Duffing oscillator begin to bifurcate. The algorithm proves to be effective in anomaly detection and warning. -
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