The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Scaling up dynamic time warping for datamining applications
Open accessAbstract
There has been much recent interest in adapting data mining algorithms to time series databases.Most of these algorithms need to compare time series.Typically some variation of Euclidean distance is used.However, as we demonstrate in this paper, Euclidean distance can be an extremely brittle distance measure.Dynamic time warping (DTW) has been suggested as a technique to allow more robust distance calculations, however it is computationally expensive.In this paper we introduce a modification of DTW which operates on a higher level abstraction of the data, in particular, a Piecewise Aggregate Approximation (PAA).Our approach allows us to outperform DTW by one to two orders of magnitude, with no loss of accuracy.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Dynamic Programming Algorithm Optimization for Spoken Word Recognition
Sakoe, H., & Chiba, S. (1978) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1978.1163055 · Cited by 6,727
Cited by
Imaging Time-Series to Improve Classification and Imputation
Wang, Z., & Oates, T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.00327 · Cited by 470
Iterative Deepening Dynamic Time Warping for Time Series
Chu, S., Keogh, E., Hart, D., & Pazzani, M. (2002) · Proceedings of the 2002 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972726.12 · Cited by 268
Self-citationAn artificial intelligence life cycle: from conception to production
De Silva, D., & Alahakoon, D. (2022) · Patterns · doi:10.1016/j.patter.2022.100489 · Cited by 189
DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection
Cao, B., Zheng, L., Zhang, C., Yu, P. S., Piscitello, A., Zulueta, J., Ajilore, O., Ryan, K., & Leow, A. D. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098086 · Cited by 145
“Distance based methods include K nearest neighbor classifier [16, 26, 27, 38, 47, 49] and SVMwith local alignment kernel [32, 41, 42] by measuring the similarity between a pair of sequences.”
MethodGeneral Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping
Ilharco, G., Jain, V., Ku, A., Ie, E., & Baldridge, J. (2019) · arXiv · doi:10.48550/arxiv.1907.05446
Metadata
- Authors
- Keogh, E. J., & Pazzani, M. J.
- Venue
- Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2000-08
- Pages
- 285-289
- OpenAlex
- W1966554111
- Cited by
- 820
- References
- 1
- FWCI
- 9.68
- Type
- conference paper
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Scaling up dynamic time warping for datamining applications. https://app.cochat.ai/papers/10.1145/347090.347153
- The paper
- Keogh, E. J., & Pazzani, M. J. (2000). Scaling up dynamic time warping for datamining applications. Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/347090.347153
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
