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School on Nonlinear Time Series Analysis and Complex Networks in the Big Data Era
February 19 - March 2
This two-week school will provide participants (mainly PhD students and postdocs) a broad overview of the state-of-the-art in the field of Big Data analysis tools, including the most recent advances in complex networks and methods for the analysis of large time series and datasets, focusing on nonlinear dynamics and network science.
Topics to be covered include:
- Time delay embedding and phase space reconstruction
- Tools for chaotic systems (Lyapunov exponents, fractal dimensions)
- Symbolic encoding techniques
- Information-theory measures (block entropy, permutation entropy, mutual information, conditional mutual information)
- Complexity measures
- Extreme value analysis
- Structure of Networks
- Mapping time series to networks (recurrence networks, visibility graphs)
- From Big data to Networks: Compression sampling, dynamical modal decomposition
Introductory lecturers will be followed by hands-on sessions where the students will have the opportunity to work in small-groups, on real-world datasets. In these sessions the participants will gain practical experience in applying the nonlinear “big-data” tools to the observed output signals of complex systems. This school is part of the topics in Nonlinear Science: Fundamentals and Applications. There is no registration fee and limited funds are available for travel and local expenses for participants from academic or research institutions.
- Alex Arenas (Universitat Rovira i Virgili, Spain)
- Murilo Baptista (University of Aberdeen, UK)
- Ernesto Estrada (University of Strathclyde, UK)
- Marta Gonzalez (MIT, U.S.A.)
- Osvaldo Rosso (Universidade Federal de Alagoas, Brazil & CONICET, Argentina)