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’
关于“通过深度学习自动检测时间序列变点”讨论的作者回复AI 解读 ↓
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AI 根据所提供的论文材料归类：所附摘要明确讨论时间序列的变点检测、自相关噪声和变化定位，但这些内容与作者回复正文的对应关系尚不明确。
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关于“通过深度学习自动检测时间序列变点”讨论的作者回复
- 研究问题
- 所给摘要讨论如何针对不同变化类型与无变化数据行为，自动构造离线变点检测方法，并评估训练数据量的作用。
- 方法与思路
- 摘要描述利用神经网络表示已有变点检验，通过训练生成检测方法，并分析误差率与训练样本量之间的关系。
- 材料陈述的贡献
- 摘要称方法在特定噪声设计中与CUSUM具有竞争力，并展示加速度数据的变化检测与定位；归属需结合回复正文核对。
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- 首先核对作者回复与所附摘要的对应关系，再确认回复实际讨论的问题，以及误差理论和经验比较属于哪份文献。
- 阅读边界
- 题名为作者回复，所给版本摘要却主要描述研究方法；不能据此认定回复新增了这些理论或结果，也不能冒充主论文解读。
原文摘要
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.