The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A review on time series data mining
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Citations
Cites
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · ACM SIGMOD Record · doi:10.1145/170036.170072 · Cited by 4,551
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
An online algorithm for segmenting time series
Keogh, E., Chu, S., Hart, D., & Pazzani, M. (2002) · Proceedings 2001 IEEE International Conference on Data Mining · doi:10.1109/icdm.2001.989531 · Cited by 1,149
Cited by
Mining electronic health records: towards better research applications and clinical care
Jensen, P. B., Jensen, L. J., & Brunak, S. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3208 · Cited by 1,680
A Review on Outlier/Anomaly Detection in Time Series Data
Blázquez-García, A., Conde, A., Mori, U., & Lozano, J. A. (2022) · ACM Computing Surveys · doi:10.1145/3444690 · Cited by 1,102
Social big data: recent achievements and new challenges
Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016) · Information Fusion · doi:10.1016/j.inffus.2015.08.005 · Cited by 846
“Based on this representation, different data mining techniques can be applied such as pattern discovery, classification, rule discovery, and summarisation [83].”
MethodDeep Learning for Time Series Forecasting: A Survey
Torres, J. F., Hadjout, D., Sebaa, A., Martínez-Álvarez, F., & Troncoso, A. (2021) · Big Data · doi:10.1089/big.2020.0159 · Cited by 818
Multivariate time series forecasting via attention-based encoder–decoder framework
Du, S., Li, T., Yang, Y., & Horng, S.-J. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.12.118 · Cited by 441
Metadata
- Authors
- Fu, T.-C.
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2011-02
- Volume
- 24
- Issue
- 1
- Pages
- 164-181
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W2106595237
- Cited by
- 1,500
- References
- 4
- FWCI
- 55.06
- Type
- journal article
- Language
- en
- Open access
- closed
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- Fu, T.-C. (2011). A review on time series data mining. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2010.09.007
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.
