Learning Temporal Causal Sequence Relationships from Real-Time Time-Series
Learning Temporal Causal Sequence Relationships from Real-Time Time-Series
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Ithaca: Cornell University Library, arXiv.org
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English
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Publisher
Ithaca: Cornell University Library, arXiv.org
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Contents
We aim to mine temporal causal sequences that explain observed events (consequents) in time-series traces. Causal explanations of key events in a time-series has applications in design debugging, anomaly detection, planning, root-cause analysis and many more. We make use of decision trees and interval arithmetic to mine sequences that explain defin...
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Full title
Learning Temporal Causal Sequence Relationships from Real-Time Time-Series
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TN_cdi_proquest_journals_2232269362
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2232269362
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E-ISSN
2331-8422
DOI
10.48550/arxiv.1905.12262
