Learning Temporal Causal Sequence Relationships from Real-Time Time-Series
Learning Temporal Causal Sequence Relationships from Real-Time Time-Series
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Publisher
San Francisco: AI Access Foundation
Journal title
Language
English
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Publisher
San Francisco: AI Access Foundation
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Scope and Contents
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 have 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 defi...
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Full title
Learning Temporal Causal Sequence Relationships from Real-Time Time-Series
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Record Identifier
TN_cdi_proquest_journals_2553248897
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2553248897
Other Identifiers
ISSN
1076-9757
E-ISSN
1076-9757,1943-5037
DOI
10.1613/jair.1.12395
