Additive Explanations for Anomalies Detected from Multivariate Temporal Data-Reference-Cited by-同舟云学术

Additive Explanations for Anomalies Detected from Multivariate Temporal Data

Published:2019-11-03 Issue: Volume: Page:
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Container-title:Proceedings of the 28th ACM International Conference on Information and Knowledge Management
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Author:

Giurgiu Ioana¹,Schumann Anika¹

Affiliation:

1. IBM Research - Zurich, Zurich, Switzerland

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3357384.3358121

Reference12 articles.

1. A. Shrikumar etal 2017a. Learning Important Features Through Propagating Activation Differences. In ICML. A. Shrikumar et al. 2017a. Learning Important Features Through Propagating Activation Differences. In ICML.

2. C. Zhang etal 2018a. A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data. In arXiv:1811.08055. C. Zhang et al. 2018a. A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data. In arXiv:1811.08055.

3. E. Gutflaish etal 2019 a. Temporal Anomaly Detection: Calibrating the Surprise. In AAAI. E. Gutflaish et al. 2019 a. Temporal Anomaly Detection: Calibrating the Surprise. In AAAI.

4. M. Sundararajan etal 2017b. Axiomatic Attribution for Deep Networks. In ICML. M. Sundararajan et al. 2017b. Axiomatic Attribution for Deep Networks. In ICML.

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