  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.
