\nCNRS\/LIG-APTIKAL — https:\/\/lig-aptikal.imag.fr\/~devijvee\/<\/a><\/p>\n
\u00c9milie Devijver is interested in statistics and machine learning, with a particular focus on structured data, including functional data and causality.<\/p>\n
Abstract<\/h2>\n
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).<\/p>\n
In this talk, we propose an overview of existing methods for inferring a causal graph for time series, and present a new method to learn an extended summary causal graph. The algorithms we propose fit within the well-known constraint-based framework for causal discovery and make use of information-theoretic measures to determine (in)dependencies between time series.
\nThe behaviour of our method is illustrated through several experiments.<\/p>\n","protected":false},"excerpt":{"rendered":"
Emilie Devijver — Causal Discovery in Observational Time Series CNRS\/LIG-APTIKAL — https:\/\/lig-aptikal.imag.fr\/~devijvee\/ \u00c9milie Devijver is interested in statistics and machine learning, with a particular focus on structured data, including functional data and causality. Abstract Time series arise as soon as observations, from sensors or experiments, for example, are collected over\u2026<\/p>\n