Robust Causal Inference for Irregularly Sampled Time Series: Applications in Climate and Paleoclimate Data Analysis

0558291 - ÚI 2023 RIV DE eng A - Abstract
Kathpalia, Aditi - Manshour, Pouya - Paluš, Milan
Robust Causal Inference for Irregularly Sampled Time Series: Applications in Climate and Paleoclimate Data Analysis.
EGU General Assembly 2022. Göttingen: European Geosciences Union, 2022.
[EGU General Assembly 2022. 23.05.2022-27.05.2022, Vienna / Online]
R&D Projects: GA ČR(CZ) GA19-16066S
Grant - others:AV ČR(CZ) AP1901
Program: Akademická prémie - Praemium Academiae
Institutional support: RVO:67985807
Keywords : causality * compression complexity * ordina patterns * Irregularly Sampled Time Series * paleoclimatology
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://meetingorganizer.copernicus.org/EGU22/EGU22-4795.html

To predict and determine the major drivers of climate has become even more important now as climate change poses a big challenge to humankind and our planet earth. Different studies employ either correlation, causality methods or modelling approaches to study the interaction between climate and climate forcing variables (anthropogenic or natural). This includes the study of interaction between global surface temperatures and CO2 rainfall in different locations and El Niño–Southern Oscillation (ENSO) phenomena. The results produced by different studies have been found to be different and debatable, presenting an ambiguous situation. In this work, we develop and apply a novel robust causality estimation technique for time-series data (to estimate causal influence between given observables), that can help to resolve the ambiguity
Permanent Link: http://hdl.handle.net/11104/0332021