OnlineSTL: Scaling Time Series Decomposition by 100x
This enables scalable real-time metrics monitoring for high-resolution data, addressing a bottleneck in time series analysis.
The paper tackled the problem of online time series decomposition for real-time monitoring by proposing OnlineSTL, which achieved 100x speedups while maintaining decomposition quality.
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.