The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
The BOSS is concerned with time series classification in the presence of noise
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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
Clustering of time series data—a survey
Warren Liao, T. (2005) · Pattern Recognition · doi:10.1016/j.patcog.2005.01.025 · Cited by 2,401
Efficient similarity search in sequence databases
Agrawal, R., Faloutsos, C., & Swami, A. (1993) · Lecture Notes in Computer Science · doi:10.1007/3-540-57301-1_5 · Cited by 1,979
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
Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases
Keogh, E., Chakrabarti, K., Pazzani, M., & Mehrotra, S. (2001) · Knowledge and Information Systems · doi:10.1007/pl00011669 · Cited by 1,573
Discovering similar multidimensional trajectories
Vlachos, M., Kollios, G., & Gunopulos, D. (2003) · Proceedings 18th International Conference on Data Engineering · doi:10.1109/icde.2002.994784 · Cited by 1,468
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
Searching and mining trillions of time series subsequences under dynamic time warping
Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., Zakaria, J., & Keogh, E. (2012) · Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2339530.2339576 · Cited by 1,047
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 need for time series data mining benchmarks: a survey and empirical demonstration
Keogh, E., & Kasetty, S. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775062 · Cited by 442
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
“Despite the fact that a huge number of parameters risks overfitting (Zhang et al., 2017) the relatively small train set in the UCR/UEA archive, our experiments showed that not only DNNs are able to significantly outperform the NN-DTW, but are also able to achieve results that are not significantly different than COTE…”
BackgroundMethodInfluentialTime 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
Dau, H. A., Bagnall, A., Kamgar, K., Yeh, C.-C. M., Zhu, Y., Gharghabi, S., Ratanamahatana, C. A., & Keogh, E. (2019) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2019.1911747 · Cited by 1,128
Metadata
- Authors
- Schäfer, P.
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2014-09-16
- Volume
- 29
- Issue
- 6
- Pages
- 1505-1530
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W1968354112
- Cited by
- 560
- References
- 22
- FWCI
- 10.77
- Type
- journal article
- Language
- en
- Open access
- closed
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- Schäfer, P. (2015). The BOSS is concerned with time series classification in the presence of noise. Data Mining and Knowledge Discovery. https://doi.org/10.1007/s10618-014-0377-7
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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.
