Time series modeling for predictive purpose has been an active research area of machine
learning for many years. However, no sufficiently comprehensive and meanwhile …

In this paper, non-homogeneous Markov-Switching Autoregressive (MS-AR) models are
proposed to describe wind time series. In these models, several autoregressive models are …

The knowledge of sea state and wind conditions is of central importance for many offshore
and nearshore operations. In this paper, we make a complete survey of stochastic models …

In this article, an original Markov‐switching autoregressive model is proposed to describe
the space–time evolution of wind fields. At first, a non‐observable process is introduced in …

The desire to group observations generated from multivariate time series is common in
many applications with the goal to distinguish not only between differences in the means of …

Dans cette thèse, plusieurs modèles originaux, utilisant les modèles autorégressifs à
change-ments de régimes markoviens, sont proposés pour les séries temporelles de vent …

A new resampling technique, referred as “local grid bootstrap”(LGB), based on
nonparametric local bootstrap and applicable to a wide range of stationary general space …

We present an automated method to detect populations of groups in galaxy redshift
catalogs. This method uses both analysis of the redshift distribution along lines of sight in …

The study of a large range of Marine dynamical systems such as structure reliability,
transport of sediment, erosion, etc. requires to understand the long term time evolution of …

The report laying in front of you is the result of an eight month research at Boskalis, the
Netherlands. Written as the completion of my study Civil Engineering at the Delft University …