OnlineSTL: Scaling Time Series Decomposition by 100x

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 o...

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Bibliographic Details
Published in:arXiv.org 2022-08
Main Authors: Mishra, Abhinav, Ram Sriharsha, Zhong, Sichen
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
Decomposition
Real time
Time response
Time series
Online Access:Get full text
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Summary: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.
ISSN:2331-8422