An Efficient Anomaly Detection in Quasi-Periodic Time Series Data—A Case Study with ECG-Reference-Cited by-同舟云学术

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An Efficient Anomaly Detection in Quasi-Periodic Time Series Data—A Case Study with ECG

Published:2018 Issue: Volume: Page:147-157
ISSN:1431-1968
Container-title:Time Series Analysis and Forecasting
language:
Short-container-title:

Author:

Chakraborty Goutam,Kamiyama Takuya,Takahashi Hideyuki,Kinoshita Tetsuo

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-96944-2_10

Reference16 articles.

1. Sha, W., Zhu, Y., Huang, T., Qiu, M., Ming, Z., Zhu, Y., Zhang Q.: A multi-order markov chain based scheme for anomaly detection. In: Computer Software and Applications Conference Workshops (COMPSACW), pp. 83–88. Shanghai Jiao TongPages (2013)

2. Ma, J., Perkins, S.: Online novelty detection on temporal sequences. In: KDD’03, Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery And Data Mining, pp. 613–618 (2003)

3. Rebbapragada, U., Protopapas, P., Brodley, C.E., Alcock, C.: Finding anomalous periodic time series. 74(3) 281–313 (2009)

4. Keogh, E., Lin, J., Fu, A.W., Van Herle, H.: Finding the unusual medical time series, algorithms and applications. EEE Trans. Inf. Technol. Biomed. 10(3), 429–439 (2006)

5. Salvador, S., Chan, P.: Learning states and rules for detecting anomalies in time series. Appl. Intell. 23(3), 241–255 (2005)

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