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FTW: Fast Similarity Search under the Time Warping Distance
Summary: FTW: an exact DTW similarity-search method that guarantees no false dismissals by introducing a novel, highly effective pruning scheme exploiting time-warp invariance. Empirically prunes many candidates and achieves up to 222× speedup versus prior SOTA.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 1366
- Venue
- PODS
- Year
- 2005
- Pagerank
- 7.0153323e-05
- Overall Rank
- 3,518 | 75.53%
- DOI
-
-
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 10 of 10 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 2,943 |
An Efficient and Accurate Method for Evaluating Time Series Similarity |
2007 |
SIGMOD |
7.8399495e-05 |
| 3,294 |
Approximate Embedding-Based Subsequence Matching of Time Series |
2008 |
SIGMOD |
7.2619257e-05 |
| 4,059 |
GRAIL: Efficient Time-Series Representation Learning |
2019 |
VLDB |
6.4854417e-05 |
| 6,074 |
Pigeonring: A Principle for Faster Thresholded Similarity Search |
2019 |
VLDB |
5.2242306e-05 |
| 7,210 |
Set-based Similarity Search for Time Series |
2016 |
SIGMOD |
4.799457e-05 |
| 8,139 |
Anticipatory DTW for Efficient Similarity Search in Time Series Databases |
2009 |
VLDB |
4.5770301e-05 |
| 8,228 |
Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data |
2007 |
VLDB |
4.5549459e-05 |
| 9,920 |
Mining and Forecasting of Big Time-series Data |
2015 |
SIGMOD |
4.2561557e-05 |
| 10,605 |
TMLKD: Few-shot Trajectory Metric Learning via Knowledge Distillation |
2025 |
VLDB |
4.1945683e-05 |
| 10,965 |
High Precision ≠ High Cost: Temporal Data Fusion for Multiple Low-Precision Sensors |
2024 |
SIGMOD |
4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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