Institute of Measurement Science
Topic
Causal analysis of measured time series
PhD. program
Measurement technology
Year of admission
2024
Name of the supervisor
RNDr. Anna Krakovská, CSc.
Contact:
Receiving school
Faculty of Electrical Engineering and Informatics at the Slovak University of Technology in Bratislava
Annotation
The topic concerns the modern analysis of causal relationships in measured data. The emphasis will be on the development of causal detection methodology, taking into account the nature of the investigated processes (stochastic, fractal, deterministic and their combinations). Diverse application areas include extensive sets of climate measurements, multi-channel electroencephalographic recordings from the human brain, temporal development of a set of macro-economic indicators and other real-world challenges of finding causal relationships from measured time series. Open problems to be explored include distinguishing between direct and indirect effects and revealing the presence of unobserved confounders. A prospective avenue in causal analysis involves assessing the differences in complexity between driving and driven systems. As a tool for estimating the level of uncertainty or information complexity, we will use, for example, Rényi's generalized dimensions and entropies, which are an extension of the classical Shannon information.
The topic is suitable for graduates interested in the design and testing of new causal methods. The successful candidate must also have good English skills, as well as experience in working in the MatLab environment. As part of the study, the doctoral student will expand his/her knowledge of methods that draw from the theory of dynamical systems, including chaos and fractal theory, and partially also from statistics, information theory and the field of machine learning.
The dissertation will be completed in a partner external educational institution, at the workplace of Institute of Measurement Science of the Slovak Academy of Sciences, v. v. i. in Bratislava.
The topic is suitable for graduates interested in the design and testing of new causal methods. The successful candidate must also have good English skills, as well as experience in working in the MatLab environment. As part of the study, the doctoral student will expand his/her knowledge of methods that draw from the theory of dynamical systems, including chaos and fractal theory, and partially also from statistics, information theory and the field of machine learning.
The dissertation will be completed in a partner external educational institution, at the workplace of Institute of Measurement Science of the Slovak Academy of Sciences, v. v. i. in Bratislava.