Detecting change-points in data is challenging because of the range of possible types of change and types of behaviour of data when there is no change. Statistically efficient methods for detecting a change will depend on both of these features, and it can be difficult for a practitioner to develop an appropriate detection method for their application of interest. We show how to automatically generate new offline detection methods based on training a neural network. Our approach is motivated by many existing tests for the presence of a change-point being representable by a simple neural network, and thus a neural network trained with sufficient data should have performance at least as good as these methods. We present theory that quantifies the error rate for such an approach, and how it depends on the amount of training data. Empirical results show that, even with limited training data, its performance is competitive with the standard CUSUM-based classifier for detecting a change in mean when the noise is independent and Gaussian, and can substantially outperform it in the presence of auto-correlated or heavy-tailed noise. Our method also shows strong results in detecting and localising changes in activity based on accelerometer data.

翻译：在数据中检测变点颇具挑战性，原因在于可能存在的变点类型以及无变点时数据行为的多样性。统计上高效的变点检测方法依赖于这两种特征，从业者往往难以针对其特定应用开发合适的检测方法。我们展示了如何通过训练神经网络来自动生成新的离线检测方法。这一方法的灵感源于许多现有的变点存在性检验可通过简单的神经网络表示，因此经过充分数据训练的神经网络至少应达到这些方法的性能水平。我们提出了量化此类方法错误率的理论，并阐明其与训练数据量的依赖关系。实验结果表明，即使训练数据有限，该方法在独立高斯噪声下检测均值变化的性能与基于CUSUM的标准分类器相当，而在存在自相关或重尾噪声的情况下则显著优于后者。此外，我们的方法在基于加速度计数据检测和定位活动变化方面也展现出优异效果。