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

With technological advancement taking place in almost every corner of the world, people are
just enjoying the increased comfort and luxury which this development is bringing with it. But …

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 …

A fuzzy clustering model for data with mixed features is proposed. The clustering model
allows different types of variables, or attributes, to be taken into account. This result is …

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 …

Timeseries representation underpin our ability to understand and predict the change of
natural system. Series are often predicated on our choice of highly redundant factors, and in …

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 …

Cokriging is a geostatistical technique that is used for spatial prediction when realizations of
a random field are available. If a secondary variable is cross-correlated with the primary …

In psychology, studying multivariate dynamical processes within a person is gaining ground.
An increasingly often used method is vector autoregressive (VAR) modeling, in which each …