Fast Time-Series Searching with Scaling and Shifting
Summary: Defines time-series similarity under per-sequence scaling and shifting and gives a geometric formulation to determine optimal scale and offset. Leverages that formulation to build a tree-based index for fast, amplitude- and offset-invariant similarity search; validated on stock-price traces. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kelvin Kam Wing Chu (Chinese University of Hong Kong)
- 2. Man Hon Wong (Chinese University of Hong Kong)
BibTeX Citation
@inproceedings{chu_pods99,
address = {New York, NY, USA},
series = {{PODS} '99},
title = {{Fast Time-Series Searching with Scaling and Shifting}},
url = {https://dl.acm.org/doi/10.1145/303976.304000},
doi = {10.1145/303976.304000},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Chu, Kelvin Kam Wing and Wong, Man Hon},
year = {1999}
}
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 198 | Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases | 2001 | SIGMOD | 0.00025558119 |
| 3,192 | General Match: A Subsequence Matching Method in Time-Series Databases Based on Generalized Windows | 2002 | SIGMOD | 7.5334132e-05 |
| 4,021 | Continually Evaluating Similarity-Based Pattern Queries on a Streaming Time Series | 2002 | SIGMOD | 6.8346215e-05 |
| 4,201 | YADING: Fast Clustering of Large-Scale Time Series Data | 2015 | VLDB | 6.7286195e-05 |
| 4,717 | Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures | 2020 | SIGMOD | 6.4388287e-05 |
| 7,574 | GAMPS: Compressing Multi Sensor Data by Grouping and Amplitude Scaling | 2009 | SIGMOD | 5.4772566e-05 |
| 7,661 | A Structured Study of Multivariate Time-Series Distance Measures | 2025 | SIGMOD | 5.4565877e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 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 | R-Trees: A Dynamic Index Structure For Spatial Searching | 1984 | SIGMOD | 0.0019892478 |
| 4 | The R*-tree: An Efficient and Robust Access Method for Points and Rectangles | 1990 | SIGMOD | 0.0011386412 |
| 42 | Fast Subsequence Matching in Time-Series Databases | 1994 | SIGMOD | 0.00045609336 |
| 90 | The X-tree: An Index Structure for High-Dimensional Data | 1996 | VLDB | 0.00034749222 |
| 282 | The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries | 1997 | SIGMOD | 0.00022250557 |
| 386 | Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases | 1995 | VLDB | 0.00019396455 |
| 812 | Dimensionality Reduction for Similarity Searching in Dynamic Databases | 1998 | SIGMOD | 0.00013710393 |
| 832 | Similarity-Based Queries for Time Series Data | 1997 | SIGMOD | 0.00013562724 |
| 4,185 | Similarity-Based Queries | 1995 | PODS | 6.7410797e-05 |
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| 1 | 198 | Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases | 2001 | SIGMOD |
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| 3 | 8,905 | The Inherent Time Complexity and An Efficient Algorithm for Subsequence Matching Problem | 2022 | VLDB |
| 4 | 8,308 | Anticipatory DTW for Efficient Similarity Search in Time Series Databases | 2009 | VLDB |
| 5 | 7,466 | Set-based Similarity Search for Time Series | 2016 | SIGMOD |
| 6 | 1,098 | A Data-adaptive and Dynamic Segmentation Index for Whole Matching on Time Series | 2013 | VLDB |
| 7 | 5,292 | Online Event-driven Subsequence Matching over Financial Data Streams | 2004 | SIGMOD |
| 8 | 832 | Similarity-Based Queries for Time Series Data | 1997 | SIGMOD |
| 9 | 42 | Fast Subsequence Matching in Time-Series Databases | 1994 | SIGMOD |
| 10 | 386 | Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases | 1995 | VLDB |