Time series anomaly detection based on shapelet learning
Time series anomaly detection based on shapelet learning
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DOI：
10.1007/s00180-018-0824-9
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发表时间：
2019-09-01
影响因子：
1.3
通讯作者：
Bischl, Bernd
中科院分区：
文献类型：
--
作者：
Beggel, Laura;Kausler, Bernhard X.;Bischl, Bernd
我们考虑了从可能被训练数据中的异常污染的未标记数据集中学习检测异常时间序列的问题。这种情况对于医学、经济学或工业质量控制中的应用非常重要，在这些应用中，标记是困难的，需要昂贵的专家知识，并且很难获得异常数据。提出了一种基于形状变换的时间序列无监督异常检测方法。我们的方法学习了源于正常类的描述时间序列形状的代表性特征，同时学习了准确地检测异常时间序列。提出了一个目标函数，它鼓励学习特征表示，其中正常时间序列位于特征空间的紧致超球面内，而异常观测将位于决策边界之外。该目标通过块坐标下降过程进行优化。该方法通过重复使用已学习的特征表示，可以在不重新训练模型的情况下，有效地检测出不可见测试数据中的异常时间序列。我们在多个基准数据集上的实验表明，我们的方法能够可靠地检测出异常时间序列，并且当训练实例中包含异常时间序列时，该方法比同类方法具有更强的鲁棒性。
We consider the problem of learning to detect anomalous time series from an unlabeled data set, possibly contaminated with anomalies in the training data. This scenario is important for applications in medicine, economics, or industrial quality control, in which labeling is difficult and requires expensive expert knowledge, and anomalous data is difficult to obtain. This article presents a novel method for unsupervised anomaly detection based on the shapelet transformation for time series. Our approach learns representative features that describe the shape of time series stemming from the normal class, and simultaneously learns to accurately detect anomalous time series. An objective function is proposed that encourages learning of a feature representation in which the normal time series lie within a compact hypersphere of the feature space, whereas anomalous observations will lie outside of a decision boundary. This objective is optimized by a block-coordinate descent procedure. Our method can efficiently detect anomalous time series in unseen test data without retraining the model by reusing the learned feature representation. We demonstrate on multiple benchmark data sets that our approach reliably detects anomalous time series, and is more robust than competing methods when the training instances contain anomalous time series.