Learning Temporal Causal Sequence Relationships from Real-Time Time-Series

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 interv...

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Bibliographic Details
Published inarXiv.org
Main Authors Antonio Anastasio Bruto da Costa, Dasgupta, Pallab
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 24.01.2021
Subjects
Computer Science - Artificial Intelligence
Computer Science - Learning
Computer Science - Logic in Computer Science
Hybrid systems
Information theory
Interval arithmetic
Sequences
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Summary: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 defining events in the time-series. We propose modified decision tree construction metrics to handle the non-determinism introduced by the temporal dimension. The mined sequences are expressed in a readable temporal logic language that is easy to interpret. The application of the proposed methodology is illustrated through various examples.
ISSN:2331-8422
DOI:10.48550/arxiv.1905.12262