The beginning of the age of artificial intelligence and machine learning has created new
challenges and opportunities for data analysts, statisticians, mathematicians …

In this paper we propose different robust fuzzy clustering models for classifying
heteroskedastic (volatility) time series, following the so-called model-based approach to time …

Time series data are commonly clustered based on their distributional characteristics. The
moments play a central role among such characteristics because of their relevant …

In this paper, following the Partitioning Around Medoids (PAM) approach and the fuzzy
theory, we propose a clustering model for financial time series based on the estimated …

The literature on time-series clustering methods has increased considerably over the last
two decades with a wide range of applications in many different fields, including geology …

This paper contributes to the existing literature on the analysis of spatial time series
presenting a new clustering algorithm called COFUST, ie COpula-based FUzzy clustering …

This paper develops a new time series clustering procedure allowing for heteroskedasticity,
non-normality and model's non-linearity. At this aim, we follow a fuzzy approach …

Clustering geographical units based on a set of quantitative features observed at several
time occasions requires to deal with the complexity of both space and time information. In …

Robustness to the presence of outliers in time series clustering is addressed. Assuming that
the clustering principle is to group realizations of series generated from similar dependence …

In this paper, we propose a new fuzzy clustering of time series with entropy regularization.
Following a model-based approach, the dissimilarity measure is based on the bivariate …