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School of Statistics for Astrophysics 2019: Variability and Time Series Analysis

This is the fourth session of the School of Statistics for Astrophysics (Stat4Astro). The topic of the 2019 session is the time series (including variabilities and transient events) that, from celestial mechanics to gravitational waves, from exoplanets to quasars, concern nearly all the astrophysics. Variable phenomena are ubiquitous in the Universe: periodic (orbits, cycles, pulses, rotations...), transient (explosions, bursts, stellar activity...), random (accretion, ejection...) or regular (apparent motions...). The detection, the characterization and the classification of these variabilities is a discipline of statistics called time series analysis. In astrophysics, the detection can be immediate to alert other telescopes, or very detailed to identify some exoplanets or probe the interior of stars. The characterization is required for the physical modeling and understanding. Classification is of course necessary to organize the observations.
When 06 October 2019 06:00 PM to
11 October 2019 02:00 PM
Where Autrans (France)
Contact Name
Attendees For PhD students, post-docs and seniors!
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The topics that will be covered are:

  • Introduction to the software environment R (Didier Fraix-Burnet IPAG/CNRS/UGA)
  • Introduction to Time Series: (mean modelisation, deseasoning, forecast by exponential smoothing (Gérard Grégoire, LJK/UGA)
  • Base tools for Time Series modelisation: stationnarity, autocovariance functions, standard and partial, frequential approach, autoregressive models, moving average, derived models (Gérard Grégoire, LJK/UGA)
  • Fourier Transform, wavelets, filtering (Marianne Clausel, Université de Lorraine)
  • Times Series: non-linear case (Eric Moulines, École Polytechnique/Académie des Sciences)
  • Machine learning, classification (Ahlame Douzal, LIG-AMA/UGA)


The registration will open in May. If you wish to be kept informed, please fill in this form https://framaforms.org/stat4astro2019-pre-registration-1485859829

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