Orateur : Émilie Devijver
Établissement : CNRS et université Grenoble-Alpes (France)
Dates : 2024-02-01 – 2024-02-01
Heures : 14:00 – 15:00
Lieu : Salle 0-3
Résumé :
Time series arise as soon as observations, from sensors or experiments, for example, are collected over time. They are present in various forms in many different domains, as healthcare (through, e.g., monitoring systems), Industry 4.0 (through, e.g., predictive maintenance and industrial monitoring systems), surveillance systems (from images, acoustic signals, seismic waves, etc.) or energy management (through, e.g. energy consumption data).
In this talk, we propose first an overview of existing methods for inferring a causal graph for time series: when we have a dataset, when and how is it possible to infer a causal graph?
Then, we discuss the causal reasoning in time series: does every causal query is identifiable from an observational dataset?