The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Time series classification with ensembles of elastic distance measures
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
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Experiencing SAX: A novel symbolic representation of time series
Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-007-0064-z · Cited by 1,618
Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., & Keogh, E. (2008) · Proceedings of the VLDB Endowment · doi:10.14778/1454159.1454226 · Cited by 1,354
Robust and fast similarity search for moving object trajectories
Chen, L., Özsu, M. T., & Oria, V. (2005) · Proceedings of the 2005 ACM SIGMOD international conference on Management of data · doi:10.1145/1066157.1066213 · Cited by 1,336
Derivative Dynamic Time Warping
Keogh, E. J., & Pazzani, M. J. (2001) · Proceedings of the 2001 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972719.1 · Cited by 1,143
Statistical Comparisons of Classifiers over Multiple Data Sets
DemšarJanez (2006) · Journal of Machine Learning Research · doi:10.5555/1248547.1248548 · Cited by 1,074
Time series shapelets: a new primitive for data mining
Ye, L., & Keogh, E. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557122 · Cited by 987
On The Marriage of Lp-norms and Edit Distance
Chen, L., & Ng, R. (2004) · Proceedings 2004 VLDB Conference · doi:10.1016/b978-012088469-8.50070-x · Cited by 774
Weighted dynamic time warping for time series classification
Jeong, Y.-S., Jeong, M. K., & Omitaomu, O. A. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2010.09.022 · Cited by 695
Cited by
Deep learning for time series classification: a review
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00619-1 · Cited by 3,342
“Lines and Bagnall (2015) compared several distance measures and showed that there is no single distance measure that significantly outperforms DTW.”
BackgroundMethodTime series classification from scratch with deep neural networks: A strong baseline
Wang, Z., Yan, W., & Oates, T. (2017) · 2017 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966039 · Cited by 2,299
Inceptiontime: finding alexnet for time series classification
Ismail Fawaz, H., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D. F., Weber, J., Webb, G. I., Idoumghar, L., Muller, P.-A., & Petitjean, F. (2020) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-020-00710-y · Cited by 1,788
Bagnall, A., Lines, J., Bostrom, A., Large, J., & Keogh, E. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0483-9 · Cited by 1,377
Self-citationJin, M., Koh, H. Y., Wen, Q., Zambon, D., Alippi, C., Webb, G. I., King, I., & Pan, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3443141 · Cited by 567
Metadata
- Authors
- Lines, J., & Bagnall, A.
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2014-06-28
- Volume
- 29
- Issue
- 3
- Pages
- 565-592
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W2050493487
- Cited by
- 522
- References
- 28
- FWCI
- 22.41
- Type
- journal article
- Language
- en
- Open access
- closed
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: Time series classification with ensembles of elastic distance measures. https://app.cochat.ai/papers/10.1007/s10618-014-0361-2
- The paper
- Lines, J., & Bagnall, A. (2015). Time series classification with ensembles of elastic distance measures. Data Mining and Knowledge Discovery. https://doi.org/10.1007/s10618-014-0361-2
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.
