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• W3186618950 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 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." @default.
• W3186618950 created "2021-08-02" @default.
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• W3186618950 date "2021-07-19" @default.
• W3186618950 modified "2023-10-17" @default.
• W3186618950 title "OnlineSTL: Scaling Time Series Decomposition by 100x" @default.
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• W3186618950 doi "https://doi.org/10.48550/arxiv.2107.09110" @default.
• W3186618950 hasPublicationYear "2021" @default.
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