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 a has applications design debugging, anomaly detection, planning, root-cause analysis and many more. make use decision trees interval arithmetic defining the time-series. propose modified tree construction metrics handle non-determinism introduced by dimension. The mined are expressed readable logic language is easy interpret. application proposed methodology illustrated through various examples.