66th ISI World Statistics Congress

66th ISI World Statistics Congress

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Statistical Advances in Time Series Modeling and Their Applications

Organiser

BN
Prof. Bouchra Nasri

Participants

  • BN
    Prof. Bouchra Nasri     (Chair)

  • N
    Prof. Sévérien Nkurunziza     (Presenter/Speaker)
  • Professor Nkurunziza’s research focuses on stochastic processes and inference methods,including change-point detection for time series.Selected references• Lyu, Y., & Nkurunziza, S. (2025). Estimation and Testing in Generalized CIR Model.Annals of Applied Probability, 35(4), 2363–2410.• Ghannam, M., & Nkurunziza, S. (2024). Change-point Detection in a Tensor RegressionModel. TEST, 33, 609–630.

  • R
    PROF. DR. Bruno Rémillard     (Presenter/Speaker)
  • Professor Rémillard has a long-standing research program in time series analysis, with a particular focus on multivariate time series using copulas. Selected references • Ghoudi, K., Nasri, B. R., & Rémillard, B. N. (2026). On Testing for Independence Between Generalized Error Models of Several Time Series. Journal of Time Series Analysis. • Guo, Q., Swishchuk, A., & Rémillard, B. N. (2024). Multivariate Hawkes-Based Models in Limit Order Books: European and Spread Option Pricing. International Journal of Theoretical and Applied Finance, 27(3–4), 2350028.

  • TB
    Taoufik Bouezmarni     (Presenter/Speaker)
  • Professor Bouezmarni has worked extensively on econometric problems related to time series modeling, particularly causality testing. Selected references • Bouezmarni, T., Doukali, M., & Taamouti, A. (2024). Testing Granger Non-Causality in Expectiles. Econometric Reviews, 43(1), 30–51. • Begin, É., Dutilleul, P., Beaulieu, C., & Bouezmarni, T. (2020). M-Vine Decomposition and VAR(1) Models. Statistics & Probability Letters, 158, 108660.

  • D
    PROF. DR. Sophie Dabo     (Presenter/Speaker)
  • Professor Nasri’s research focuses on multivariate time series modeling and statistical inference, including change-point detection and independence testing, with applications to climate change and infectious disease modeling. Selected references • Nasri, B. R., & Rémillard, B. N. (2024). Tests of Independence and Randomness for Arbitrary Data Using Copula-Based Covariances. Journal of Multivariate Analysis, 201, 105273. • Nasri, B. R. (2022). Tests of Serial Dependence for Multivariate Time Series with Arbitrary Distributions. Journal of Multivariate Analysis, 192, 105102.

  • SM
    Solym Manou-Abi     (Presenter/Speaker)
  • Professor Manou-Abi has contributed to the development of statistical methods for stochastic processes and time series modeling, with a focus on infectious disease threats. Selected references • Bouzalmat, I., de Saporta, B., & Manou-Abi, S. M. (2026). Inference for Hidden Stochastic Compartmental Models: Application to Typhoid Fever Dynamics in Mayotte. Journal of the Royal Statistical Society: Series C (Applied Statistics), 75(3), 649–676. • Raherinirina, A., et al. (2025). Bayesian Inference of a Spatially Dependent Semi- Markovian Model with Application to Madagascar COVID-19 Data. PLoS ONE, 20(7), e0326264.

    • Conference

    66th ISI World Statistics Congress

    Proposal Description

    The use of time series models for multivariate data generally requires testing one or several
    fundamental assumptions. For example, one may wish to assess assumptions regarding the
    distribution of innovations through goodness-of-fit tests or verify stationarity through changepoint
    tests. From an applied perspective, testing serial independence between innovations or
    among time series is often necessary to determine whether a fitted model has adequately
    removed temporal dependence. Another important challenge is the detection of online changepoints,
    that is, identifying structural changes as new observations become available. This is
    particularly relevant for early warning systems in infectious disease surveillance, as well as for
    applications in finance and econometrics.
    The goal of this session is to present recent advances in statistical inference and testing for time
    series, including inference for multivariate time series, goodness-of-fit testing, and change-point
    detection.
    The invited speakers have made outstanding contributions to time series modeling and related
    applications across a range of domains.

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