Multivariate time series (MTS) data are widely available in di erent elds including medicine, nance, science and engineering. Modelling MTS data e ectively is important for many decision-making activities. In this paper, we will describe some of our e orts in modelling these data for numerous tasks such as outlier analysis, forecasting and explanation. Through the analysis of various kinds of M...

We introduce graphical time series models for the analysis of dynamic relationships among variables in multivariate time series. The modelling approach is based on the notion of strong Granger causality and can be applied to time series with non-linear dependencies. The models are derived from ordinary time series models by imposing constraints that are encoded by mixed graphs. In these graphs ...

Abstract: A multivariate, stationary time series is said to be jointly regularly varying if all its finite-dimensional distributions are multivariate regularly varying. This property is shown to be equivalent to weak convergence of the conditional distribution of the rescaled series given that, at a fixed time instant, its distance to the origin exceeds a threshold tending to infinity. The limi...