Learning Temporal Causal Sequence Relationships from Real-Time Time-Series
This work addresses the need for causal explanations in time-series for applications like debugging and anomaly detection, though it appears incremental as it builds on existing decision tree methods with modifications for temporal aspects.
The paper tackles the problem of mining temporal causal sequences from real-time time-series to explain observed events, using modified decision trees and interval arithmetic to handle temporal non-determinism, resulting in sequences expressed in an interpretable temporal logic language.
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.