Time series clustering is the act of grouping 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 …

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 …

With the progressive advancement of the smart grid paradigm, electricity systems worldwide
are deploying advanced metering technologies in residential sectors. Smart meters can …

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 …

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 …

We study the problem of constructing coresets for clustering problems with time series data.
This problem has gained importance across many fields including biology, medicine, and …

We investigate the fuzzy clustering of interval time series using wavelet variances and
covariances; in particular, we use a fuzzy c-medoids clustering algorithm. Traditional …