Change point detection (CPD) aims to locate abrupt property changes in time series data. Recent CPD methods demonstrated the potential of using deep learning techniques, but often lack the ability to identify more subtle changes in the autocorrelation statistics of the signal and suffer from a high false alarm rate. To address these issues, we employ an autoencoder-based methodology with a novel loss function, through which the used autoencoders learn a partially time-invariant representation that is tailored for CPD. The result is a flexible method that allows the user to indicate whether change points should be sought in the time domain, frequency domain or both. Detectable change points include abrupt changes in the slope, mean, variance, autocorrelation function and frequency spectrum. We demonstrate that our proposed method is consistently highly competitive or superior to baseline methods on diverse simulated and real-life benchmark data sets. Finally, we mitigate the issue of false detection alarms through the use of a postprocessing procedure that combines a matched filter and a newly proposed change point score. We show that this combination drastically improves the performance of our method as well as all baseline methods.

中文翻译：

---

使用具有时不变表示的自动编码器检测时间序列数据中的变化点


变化点检测（CPD）旨在定位时间序列数据中的突然属性变化。最近的 CPD 方法展示了使用深度学习技术的潜力，但通常缺乏识别信号自相关统计数据中更细微变化的能力，并且误报率很高。为了解决这些问题，我们采用基于自动编码器的方法和新颖的损失函数，通过该方法，使用的自动编码器学习专为 CPD 定制的部分时不变表示。结果是一种灵活的方法，允许用户指示是否应在时域、频域或两者中寻找变化点。可检测的变化点包括斜率、均值、方差、自相关函数和频谱的突然变化。我们证明，我们提出的方法始终具有高度竞争力或优于各种模拟和现实基准数据集上的基线方法。最后，我们通过使用结合匹配滤波器和新提出的变化点得分的后处理程序来减轻错误检测警报的问题。我们表明，这种组合极大地提高了我们的方法以及所有基线方法的性能。