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 …

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

The statistical properties of the autoregressive (AR) distance between ARIMA processes are
investigated. In particular, the asymptotic distribution of the squared AR distance and an …

This paper presents a test of hypotheses to compare two stationary time seriesas well as an
accompanying classification procedure that uses this test ofhypotheses to cluster stationary …

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 dream of a multiattribute approach to empirical welfare analysis is coming closer to
reality because of significant advances in both theoretical and measurement areas. In this …

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 …