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OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting

Summary: OneShotSTL: an online, one-shot seasonal-trend decomposition algorithm with O(1) per-update cost (vs. batch O(W)), enabling low-latency real-time time-series analysis. Achieves 10–1,000× speedups over SOTA while maintaining comparable or better anomaly-detection and forecasting accuracy. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13191
Venue
VLDB
Year
2023
Pagerank
5.4119882e-05
Overall Rank
8,517 | 41.57%
DOI
10.14778/3583140.3583155

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{he_vldb23,
        title = {{OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting}},
        author = {He, Xiao and Li, Ye and Tan, Jian and Wu, Bin and Li, Feifei},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {6},
        pages = {1399--1412},
        doi = {10.14778/3583140.3583155},
        url = {https://doi.org/10.14778/3583140.3583155},
        year = {2023}
}

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