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)
Incoming Non-self Citations Over Time
Authors
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,934 | SimpleTS: An Efficient and Universal Model Selection Framework for Time Series Forecasting | 2023 | VLDB | 6.6175631e-05 |
| 6,765 | Automatic Database Configuration Debugging using Retrieval-Augmented Language Models | 2025 | SIGMOD | 4.9325583e-05 |
| 8,658 | Modernization of Databases in the Cloud Era: Building Databases that Run like Legos | 2023 | VLDB | 4.4729338e-05 |
| 9,682 | Lindorm TSDB: A Cloud-native Time-series Database for Large-scale Monitoring Systems | 2023 | VLDB | 4.3047774e-05 |
| 10,061 | Cleaning Time Series under Seasonal and Trend Constraints | 2026 | SIGMOD | 4.1945683e-05 |
| 10,611 | ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning | 2025 | VLDB | 4.1945683e-05 |
| 10,704 | Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services | 2025 | VLDB | 4.1945683e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,029 | SAND: Streaming Subsequence Anomaly Detection | 2021 | VLDB | 9.740868e-05 |
| 2,290 | TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data | 2022 | VLDB | 9.0934125e-05 |
| 2,381 | TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection | 2022 | VLDB | 8.9327638e-05 |
| 3,943 | Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection | 2022 | VLDB | 6.6099833e-05 |
| 4,113 | RobustPeriod: Robust Time-Frequency Mining for Multiple Periodicity Detection | 2021 | SIGMOD | 6.4420064e-05 |
| 6,204 | OnlineSTL: Scaling Time Series Decomposition by 100x | 2022 | VLDB | 5.1590612e-05 |
Previous
Page 1 / 1
Next