New Results on Time Series and their Statistical Applications
Séries chronologiques: nouveaux résultats et applications statistiques

HYBRID CONFERENCE /  14 – 18 September 2020
(With abstracts)
(Marseille, France local time)
Discussion rooms will be available to registered conference participants via the link below. Your access code to the rooms is provided by the organizers.
This page is accessible with a password issued by CIRM.

Organizing Committee

Jean-Marc Bardet (Université Paris 1 Panthéon-Sorbonne)
​Idris Eckley (Lancaster University)
Konstantinos Fokianos (University of Cyprus)
Michael H. Neumann (Friedrich Schiller University Jena)
Anne Philippe (Université de Nantes

Scientific Committee

Paul Doukhan (​Université de Cergy-Pontoise)
Liudas Giraitis (Queen Mary University of London)
Suhasini Subba Rao (Texas A&M University)
Olivier Winterberger (Sorbonne Université)

Statistical modeling and analysis of time series data has been traditionally based on the assumption of stationarity and/or low dimensionality. Nowadays technology calls for abandoning such assumptions and require the development of new and more sophisticated statistical methods. For instance, the assumption of stationarity, which has dominated most of time series literature, is restricted and cannot be justified for many applications. Additionally, study of long-range dependent processes revealed concrete distinctions between stationary and non-stationary processes. And it is quite common in practice to observe time series which exhibit trend, periodic behavior and require additionally inclusion of covariates. 

These new applications are developing very quickly and are found in diverse research areas. Based on this fact and motivated by these challenging problems, we propose to organize a conference whose main theme is New results on time series and their statistical applications. We are quite confident that such a meeting will bring together researchers from all over the world to discuss and exchange ideas about future research directions and initiatives.

In particular, we aim on a program that covers many ”hot” research topics like: 
​• Locally-stationary and non-stationary time series; 
• High-dimensional time series;
• Change-point methods and switching processes; 
• Long-range dependent time series; 
• New limit theorems on means and extremes; 
• New concentration inequalities and model selection; 
• New measures of dependence; 
• New statistical applications on reall datasets


  • Alexander Aue (University of California, Davis) Relevant two-sample tests for the eigenfunctions of covariance operators
  • Felix Cheysson (AgroParis Tech) Estimation of Hawkes processes from binned observations using Whittle likelihood
  • Richard Davis (Columbia University) Modeling of Time Series Using Random Forests: Theoretical Developments
  • Herold Dehling (Ruhr University Bochum) An Asymptotic Test for Constancy of Variance under Short-Range Dependence
  • Holger Dette (Ruhr University Bochum)  Relevant hypotheses in functional data
  • Youssef Esstafa (Université de Franche-Comté) Estimating FARIMA models with uncorrelated but non-independent error terms
  • Christian Francq (CREST) Adaptiveness of the empirical distribution of residuals in semi-parametric conditional location scale models.
  • Liudas Giraitis (Queen Mary University of London) Time-Varying Instrumental Variable Estimation
  • Yannig Goude (EDF R&D, Université Paris-Saclay) Machine learning methods for electricity load forecasting: contributions and perspectives
  • Kamila Kare (Université Panthéon Sorbonne) Consistent model selection criteria and goodness-of-fit test for common time series models
  • Claudia Kirch (Otto von Guericke University of Magdeburg) Beyond Whittle’s likelihood: New Bayesian semiparametric approaches to time series analysis
  • Claudia Klueppelberg (Technical University of Munich) Indirect Inference for Time Series Using the Empirical Characteristic Function and Control Variates
  • Clifford Lam (The London School of Economics and Political Science) Robust mean and Eigenvalues regularized covariance matrix estimation
  • Émilie Lebarbier (Université Paris Nanterre)  A factor model approach for the joint segmentation of correlated series
  • Kathryn Leeming (University of Warwick) Modelling farm temperatures with irregular seasonality
  • Remigijus Leipus (Vilnius University) Estimating and testing long memory in random coefficient dynamic panel data model
  • Anne Leucht (University of Bamberg) Testing Equality of Spectral Density Operators for Functional Processes
  • Philippe Naveau (CNRS, Université Versailles Saint Quentin) Detecting seasonality changes in multivariate extremes from climatological time series
  • Riccardo Passeggeri  (Sorbonne Université) Asymptotic analysis of extremes of general stationary spatio-temporal models
  • Joseph Rynkiewicz (Université Panthéon Sorbonne) Mixtures of Nonlinear Poisson Autoregressions
  • Philippe Soulier (Université Paris Nanterre) The tail process and tail measure of continuous time regularly varying stochastic processes
  • Suhasini Subba Rao (Texas A&M University) Reconciling the Gaussian and Whittle Likelihood with an application to estimation in the frequency domain
  • Lionel Truquet (ENSAI) Stationarity and ergodic properties for some observation-driven models in random environments
  • Almut Veraart (Imperial College London)  Likelihood-based estimation, model selection, and forecasting of integer-valued trawl processes
  • Rainer von Sachs (Université Catholique de Louvain) Intrinsic wavelet smoothing of curves of Hermitian positive definite matrices (with applications to spectral density estimation of multivariate time series)
  • Jean-Michel Zakoian (CREST) Testing the existence of moments for GARCH-type processes.
  • Zhou Zhou (University of Toronto) Frequency Detection and Change Point Estimation for Time Series of Complex Oscillation