Time series clustering is an active research area with applications in a wide range of fields.
One key component in cluster analysis is determining a proper dissimilarity measure …

Most clustering strategies have not changed considerably since their initial definition. The
common improvements are either related to the distance measure used to assess …

Clustering is a powerful vehicle to reveal and visualize structure of data. When dealing with
time series, selecting a suitable measure to evaluate the similarities/dissimilarities within the …

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

Time Series is an orderly sequence of values of a variable in a particular domain.
Forecasting is a challenging task in the area of Time Series Analysis. Forecasting has a …

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 …

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 …

Detecting incident anomalies within temporal data-time series becomes useful in a variety of
applications. In this paper, anomalies in time series are divided into two categories, namely …

In spatiotemporal data commonly encountered in geographical systems, biomedical signals,
and the like, each datum is composed of features comprising a spatial component and a …