Learning Generalized Causal Structure in Time-series

0556948 - ÚI 2023 US eng V - Research Report
Kathpalia, Aditi - Charantimath, K. P. - Nagaraj, N.
Learning Generalized Causal Structure in Time-series.
Cornell University, 2021. 10 s. arXiv.org e-Print archive, arXiv:2112.03085.
R&D Projects: GA ČR(CZ) GA19-16066S
Grant - others:AV ČR(CZ) AP1901
Program: Akademická prémie - Praemium Academiae
Institutional support: RVO:67985807
https://arxiv.org/abs/2112.03085

The science of causality explains/determines 'cause-effect' relationship between the entities of a system by providing mathematical tools for the purpose. In spite of all the success and widespread applications of machine-learning (ML) algorithms, these algorithms are based on statistical learning alone. Currently, they are nowhere close to 'human-like' intelligence as they fail to answer and learn based on the important "Why?" questions. Hence, researchers are attempting to integrate ML with the science of causality. Among the many causal learning issues encountered by ML, one is that these algorithms are dumb to the temporal order or structure in data. In this work we develop a machine learning pipeline based on a recently proposed 'neurochaos' feature learning technique (ChaosFEX feature extractor), that helps us to learn generalized causal-structure in given time-series data.
Permanent Link: http://hdl.handle.net/11104/0331066