References
Zhang Q, Hu Y P, Ji C, et al. Edge computing application: real-time anomaly detection algorithm for sensing data. J Comput Res Dev, 2018, 55: 524–536
Hu Y, Zhan P, Xu Y, et al. Temporal representation learning for time series classification. Neural Comput Appl, 2020, 32: 1–14
Hu Y, Ji C, Zhang Q, et al. A novel multi-resolution representation for time series sensor data analysis. Soft Comput, 2020, 24: 10535–10560
Zhan P, Sun C, Hu Y, et al. Feature-based online representation algorithm for streaming time series similarity search. Int J Patt Recogn Artif Intell, 2020, 34: 2050010
Hu Y, Ren P, Luo W, et al. Multi-resolution representation with recurrent neural networks application for streaming time series in IoT. Comput Netw, 2019, 152: 114–132
Keogh E, Lin J, Fu A W, et al. Finding unusual medical time-series subsequences: algorithms and applications. IEEE Trans Inform Technol Biomed, 2006, 10: 429–439
Keogh E, Chakrabarti K, Pazzani M, et al. Dimensionality reduction for fast similarity search in large time series databases. Knowledge Inf Syst, 2001, 3: 263–286
Ren H, Liao X, Li Z, et al. Anomaly detection using piece-wise aggregate approximation in the amplitude domain. Appl Intell, 2018, 48: 1097–1110
Acknowledgements
This work was supported by National Key Research Program of China (Grant No. U1936203), Shandong Provincial Natural Science and Foundation (Grant No. ZR2019JQ23), CERNET Innovation Project (Grant No. NGII20190109), and Project of Qingdao Postdoctoral Applied Research.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhan, P., Hu, Y., Chen, L. et al. SPAR: set-based piecewise aggregate representation for time series anomaly detection. Sci. China Inf. Sci. 64, 149101 (2021). https://doi.org/10.1007/s11432-020-3021-6
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11432-020-3021-6