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：检测数据中的变点具有挑战性，因为可能的变点类型及无变点时数据的行为模式种类繁多。统计高效的变点检测方法同时依赖于上述两种特征，而从业人员往往难以针对其特定应用开发出合适的检测方法。本文展示了如何通过训练神经网络自动生成新的离线检测方法。该方法的动机在于：许多现有的变点存在性检验可表示为简单的神经网络，因此经过充足数据训练的神经网络应至少具备与这些方法相当的性能。我们提出了量化该方法误差率及其与训练数据量依存关系的理论。实验结果表明，即使训练数据有限，该方法在独立高斯噪声环境下检测均值变化时的性能仍可与基于CUSUM的标准分类器相媲美，并在存在自相关或重尾噪声时显著优于后者。此外，该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性能。