Discovering Cause-Effect Relationships between Time Series in Non-Gaussian Noise Environments

Links

https://www.scopus.com/inward/record.uri?eid=2-s2.0-105029682687&doi=10.1016%2Fj.ifacol.2025.12.140&partnerID=40&md5=54c81dcb0b08a98df92ada690c42d7a6

DOI

https://doi.org/10.1016/j.ifacol.2025.12.140
Final published version

We address the problem of detecting relationships between time series data in the presence of noise. We formalize the problem of searching for relationships, examine different approaches, and develop a method that more efficiently detects relationships in the presence of non-standard noise. © © 2025 The Authors.

Original language	English
Pages (from-to)	145-149
Number of pages	5
Journal	IFAC-PapersOnLine
Volume	59
Issue number	14
DOIs	https://doi.org/10.1016/j.ifacol.2025.12.140
State	Published - 2025
Event	15th IFAC Workshop on Adaptive and Learning Control Systems - Mexico, Mexico Duration: 2 Jul 2025 → 4 Jul 2025

Research areas

Bounded disturbances, Bounded noise, Estimation algorithms, Time-series analysis, Artificial intelligence, Background noise, Gaussian noise (electronic), Information systems, Information use, Signal processing, Cause-effect relationships, Estimation algorithm, Noise environments, Non-Gaussian noise, Time-series data, Times series, Copyrights, Time series analysis

ID: 148838127