Authors' reply to the Discussion of 'Automatic Change-Point Detection in Time Series via Deep Learning' at the Discussion Meeting on 'Probabilistic and statistical aspects of machine learning'
Authors' reply to the Discussion of 'Automatic Change-Point Detection in Time Series via Deep Learning' at the Discussion Meeting on 'Probabilistic and statistical aspects of machine learning'
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作者在“机器学习的概率和统计方面”讨论会上对“通过深度学习自动检测时间序列变化点”的讨论的回复
DOI：
10.1093/jrsssb/qkae008
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发表时间：
2024
影响因子：
--
通讯作者：
Li J
中科院分区：
文献类型：
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作者：
Li J
检测数据中的变化点是具有挑战性的，因为可能的变化类型的范围和数据在没有变化时的行为类型。用于检测变化的统计上有效的方法将取决于这两个特征，从业者可能很难为他们感兴趣的应用开发适当的检测方法。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。我们的方法是基于许多现有的测试，以确定一个简单的神经网络是否存在变化点，因此，一个经过足够数据训练的神经网络应该至少具有与这些方法一样好的性能。我们提出了量化这种方法的错误率的理论，以及它如何依赖于训练数据量。实验结果表明，即使在训练数据有限的情况下，它的性能与基于标准累积和(CUSUM)的分类器相当，当噪声是独立的和高斯的时，它在检测均值变化方面的性能与标准累积和(CUSUM)分类器相当，而在存在自相关或重尾噪声的情况下，它的性能明显优于标准累积和(CUSUM)分类器。我们的方法在检测和定位基于加速度计数据的活动变化方面也显示出很强的结果。
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 cumulative sum (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 localizing changes in activity based on accelerometer data.