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Anticipatory DTW for Efficient Similarity Search in Time Series Databases

Summary: Anticipatory DTW reuses filter-step info during refinement to prune candidates earlier. It leverages state-of-the-art DTW lower bounds with negligible overhead, no false dismissals, and scales to multivariate, long series and wide bands. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10136
Venue
VLDB
Year
2009
Pagerank
5.4851245e-05
Overall Rank
8,107 | 44.38%
DOI
10.14778/1687627.1687721

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{assent_vldb09,
        title = {{Anticipatory DTW for Efficient Similarity Search in Time Series Databases}},
        author = {Assent, Ira and Wichterich, Marc and Krieger, Ralph and Kremer, Hardy and Seidl, Thomas},
        journal = {PVLDB},
        series = {{VLDB} '09},
        doi = {10.14778/1687627.1687721},
        url = {https://doi.org/10.14778/1687627.1687721},
        year = {2009}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
6,858 A Generic Framework for Efficient and Effective Subsequence Retrieval 2012 VLDB 5.7523396e-05
8,026 A New Approach for Processing Ranked Subsequence Matching Based on Ranked Union 2011 SIGMOD 5.5049255e-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.

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