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

## TL;DR

This paper introduces a method to mine and interpret temporal causal sequences from time-series data using decision trees and interval arithmetic, aiding in various analytical applications.

## Contribution

It presents a novel approach combining decision trees and interval arithmetic to extract readable temporal causal sequences from time-series data.

## Key findings

- Effective extraction of causal sequences demonstrated on multiple examples.
- Sequences are expressed in an interpretable temporal logic language.
- Modified decision tree metrics handle temporal non-determinism.

## 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.