Learning Temporal Causal Sequence Relationships from Real-Time Time-Series
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 have 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.
Cite
Text
da Costa and Dasgupta. "Learning Temporal Causal Sequence Relationships from Real-Time Time-Series." Journal of Artificial Intelligence Research, 2021. doi:10.1613/JAIR.1.12395Markdown
[da Costa and Dasgupta. "Learning Temporal Causal Sequence Relationships from Real-Time Time-Series." Journal of Artificial Intelligence Research, 2021.](https://mlanthology.org/jair/2021/dacosta2021jair-learning/) doi:10.1613/JAIR.1.12395BibTeX
@article{dacosta2021jair-learning,
title = {{Learning Temporal Causal Sequence Relationships from Real-Time Time-Series}},
author = {da Costa, Antonio Anastasio Bruto and Dasgupta, Pallab},
journal = {Journal of Artificial Intelligence Research},
year = {2021},
pages = {205-243},
doi = {10.1613/JAIR.1.12395},
volume = {70},
url = {https://mlanthology.org/jair/2021/dacosta2021jair-learning/}
}