Optimal Multi-scale Patterns in Time Series Streams
Summary: Multi-scale pattern discovery in time-series streams: learns local patterns across window sizes with a criterion to capture oscillatory/aperiodic trends. Novelty: learn a data-driven orthonormal transform; no fixed bases, enabling fast incremental streaming with order-of-magnitude savings. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,219 | Matrix Profile X: VALMOD - Scalable Discovery of Variable-Length Motifs in Data Series | 2018 | SIGMOD | 6.3500768e-05 |
| 4,476 | Classical and Contemporary Approaches to Big Time Series Forecasting | 2019 | SIGMOD | 6.1517903e-05 |
| 4,888 | Forecasting Big Time Series: Old and New | 2018 | VLDB | 5.8531064e-05 |
| 6,535 | Effective Variation Management for Pseudo Periodical Streams | 2007 | SIGMOD | 5.0243433e-05 |
| 6,774 | Matrix Sketching Over Sliding Windows | 2016 | SIGMOD | 4.9299348e-05 |
| 9,324 | LightCTS: A Lightweight Framework for Correlated Time Series Forecasting | 2023 | SIGMOD | 4.3556432e-05 |
| 9,586 | A Skip-list Approach for Efficiently Processing Forecasting Queries | 2008 | VLDB | 4.3218691e-05 |
| 9,920 | Mining and Forecasting of Big Time-series Data | 2015 | SIGMOD | 4.2561557e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 65 | Fast Subsequence Matching in Time-Series Databases | 1994 | SIGMOD | 0.00062029383 |
| 243 | Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases | 2001 | SIGMOD | 0.00031074984 |
| 1,346 | Streaming Pattern Discovery in Multiple Time-Series | 2005 | VLDB | 0.00012466288 |
| 2,448 | Multi-Dimensional Regression Analysis of Time-Series Data Streams | 2002 | VLDB | 8.8032353e-05 |
| 2,477 | Identifying Similarities, Periodicities and Bursts for Online Search Queries | 2004 | SIGMOD | 8.6941234e-05 |
| 3,121 | Compressing Historical Information in Sensor Networks | 2004 | SIGMOD | 7.5271941e-05 |
| 3,794 | Identifying Representative Trends in Massive Time Series Data Sets Using Sketches | 2000 | VLDB | 6.7617267e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,665 | Rare Time Series Motif Discovery from Unbounded Streams | 2015 | VLDB | 5.3821303e-05 |
| 8,286 | OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting | 2023 | VLDB | 4.5435639e-05 |
| 6,204 | OnlineSTL: Scaling Time Series Decomposition by 100x | 2022 | VLDB | 5.1590612e-05 |
| 2,448 | Multi-Dimensional Regression Analysis of Time-Series Data Streams | 2002 | VLDB | 8.8032353e-05 |
| 10,599 | Time Series Motif Discovery: A Comprehensive Evaluation | 2025 | VLDB | 4.1945683e-05 |
| 662 | A Framework for Clustering Evolving Data Streams | 2003 | VLDB | 0.00018475968 |
| 1,346 | Streaming Pattern Discovery in Multiple Time-Series | 2005 | VLDB | 0.00012466288 |
| 3,338 | Fast Time-Series Searching with Scaling and Shifting | 1999 | PODS | 7.2040692e-05 |
| 4,210 | Continually Evaluating Similarity-Based Pattern Queries on a Streaming Time Series | 2002 | SIGMOD | 6.3566839e-05 |
| 3,794 | Identifying Representative Trends in Massive Time Series Data Sets Using Sketches | 2000 | VLDB | 6.7617267e-05 |