OnlineSTL: Scaling Time Series Decomposition by 100x
Summary: OnlineSTL provides scalable decomposition of time series into trend, seasonality, and remainder for anomaly and change-point detection on high-ingest data. Delivers 100x speedups for large seasonalities with preserved quality, enabling streaming metrics. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Abhinav Mishra (Splunk)
- 2. Ram Sriharsha (Pinecone)
- 3. Sichen Zhong (Splunk)
BibTeX Citation
@article{mishra_vldb22,
title = {{OnlineSTL: Scaling Time Series Decomposition by 100x}},
author = {Mishra, Abhinav and Sriharsha, Ram and Zhong, Sichen},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {7},
pages = {1417--1425},
doi = {10.14778/3523210.3523219},
url = {https://doi.org/10.14778/3523210.3523219},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,517 | OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting | 2023 | VLDB | 5.4119882e-05 |
| 9,940 | RALF: Accuracy-Aware Scheduling for Feature Store Maintenance | 2024 | VLDB | 5.1924403e-05 |
| 10,353 | Cleaning Time Series under Seasonal and Trend Constraints | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 148 | Gorilla: A Fast, Scalable, In-Memory Time Series Database | 2015 | VLDB | 0.00029250767 |
| 3,123 | Monarch: Google’s Planet-Scale In-Memory Time Series Database | 2020 | VLDB | 7.7354933e-05 |
| 4,345 | ASAP: Prioritizing Attention via Time Series Smoothing | 2017 | VLDB | 6.7513816e-05 |
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