Time series clustering is the act of grou** time series data without recourse to a label.
Algorithms that cluster time series can be classified into two groups: those that employ a time …

In this paper, we apply a functional clustering method to the multivariate time series of life
expectancy at birth of the female populations collected in the Human Mortality Database. We …

We study how the COVID-19 pandemic affected some of the conditional volatilities of S&P
500 industries, using a new model feature-based clustering method on a fitted TGARCH …

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 …

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 …

This paper focuses on the real-time dynamic clustering analysis of power load data based
on the dynamic conditional score (DCS) model, and an autocorrelation increment fuzzy C …

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

The domain of cluster analysis is a meeting point for a very rich multidisciplinary encounter,
with cluster‐analytic methods being studied and developed in discrete mathematics …

Hydrograph clustering helps to identify dynamic patterns within aquifers systems, an
important foundation of characterizing groundwater systems and their influences, which is …

This article analyzes how Big Data is changing the way we learn from observations. We
describe the changes in statistical methods in seven areas that have been shaped by the …