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arXiv · 1905.12262

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

Antonio Anastasio Bruto da Costa·Pallab Dasgupta
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Abstract

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.

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BibTeXRIS

Antonio Anastasio Bruto da Costa, Pallab Dasgupta. 2019-05-29. Learning Temporal Causal Sequence Relationships from Real-Time Time-Series. https://doi.org/10.1613/jair.1.12395

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