在线学习模型实时检测时间序列异常，适应数据波动变化。
OML-AD: Online Machine Learning for Anomaly Detection in Time Series Data
- 采用在线机器学习方法，动态更新模型应对非平稳数据
- 在多个真实数据集上准确率优于现有主流方法
- 适合工业传感器、金融流等需实时处理的场景
时间序列广泛存在于制造过程、金融数据流和气候观测中。为可靠解决回归、分类或分割任务，需剔除偏离正常行为的异常观测。现有异常检测方法多针对独立数据或平稳时间序列，难以应对非平稳数据。为此，本文提出 OML-AD，一种基于在线机器学习（OML）的新型异常检测方法。该方法可在数据持续变化时不断更新模型，实现对非平稳时间序列的高效异常识别。我们在 Python 库 River 中实现了 OML-AD，实验表明其在准确率和计算效率上均优于当前最优基线方法，在真实数据集（如 ETT、Yahoo A1、SMD）上表现突出。
原文摘要 · Abstract (English)
Time series are ubiquitous and occur naturally in a variety of applications -- from data recorded by sensors in manufacturing processes, over financial data streams to climate data. Different tasks arise, such as regression, classification or segmentation of the time series. However, to reliably solve these challenges, it is important to filter out abnormal observations that deviate from the usual behavior of the time series. While many anomaly detection methods exist for independent data and stationary time series, these methods are not applicable to non-stationary time series. To allow for non-stationarity in the data, while simultaneously detecting anomalies, we propose OML-AD, a novel approach for anomaly detection (AD) based on online machine learning (OML). We provide an implementation of OML-AD within the Python library River and show that it outperforms state-of-the-art baseline methods in terms of accuracy and computational efficiency.
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