OnlineSTL: Scaling Time Series Decomposition by 100x

• URL: http://arxiv.org/abs/2107.09110v1
• Date: Mon, 19 Jul 2021 19:03:27 GMT
• Title: OnlineSTL: Scaling Time Series Decomposition by 100x
• Authors: Abhinav Mishra, Ram Sriharsha, Sichen Zhong
• Abstract summary: OnlineSTL is a novel online algorithm for time series decomposition. OnlineSTL is deployed for real-time metrics monitoring on high resolution, high ingest rate data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 solves the scalability problem 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 while maintaining quality of decomposition.

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