2021/07/19 by Abhinav Mishra, Mishra, Abhinav, Ram Sriharsha +3
Computer Science · Economics, Econometrics and Finance · #Anomaly Detection Techniques and Applications #Complex Systems and Time Series Analysis #D.2.8 #Databases (cs.DB) #F.2 #FOS: Computer and information sciences #H.2 #I.2.1 #I.5.1 #Machine Learning (cs.LG) #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2107.09110
openalex publication_date 2021/07/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.