Decomposing a complex time series into trend, seasonality, and remainder components is an important primitive that facilitates time series anomaly detection, change point detection, and forecasting. Although numerous batch algorithms are known for time series decomposition, none operate well in an online scalable setting where high throughput and real-time response are paramount. In this paper, we propose OnlineSTL, a novel online algorithm for time series decomposition which is highly scalable and is deployed for real-time metrics monitoring on high-resolution, high-ingest rate data. Experiments on different synthetic and real world time series datasets demonstrate that OnlineSTL achieves orders of magnitude speedups (100x) while maintaining quality of decomposition.

翻译：将复杂的时间序列分解成趋势、季节性和剩余部分是重要的原始元素,有助于时间序列异常检测、改变点检测和预测。 虽然已知的批量算法是时间序列分解的,但没有一个在可扩展的在线环境中运行良好,因为高吞吐量和实时反应是最重要的。 在本文中,我们提议在线STL(OnlineSTL),这是一个用于时间序列分解的新颖的在线算法,它可高度缩放,用于实时监测高分辨率和最高摄取率数据。 对不同合成和真实世界时间序列数据集的实验表明,在线STL在保持分解质量的同时,实现了数量级加速(100x) 。