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

A novel procedure to perform fuzzy clustering of multivariate time series generated from
different dependence models is proposed. Different amounts of dissimilarity between the …

Although there are many contributions in the time series clustering literature, few studies still
deal with count time series data. This paper aims to develop a fuzzy clustering procedure for …

Time series classification is an important topic in data mining. Time series classification
methods have been studied by many researchers. A feature-weighted classification method …

Two novel distances between categorical time series are introduced. Both of them measure
discrepancies between extracted features describing the underlying serial dependence …

Diagnostic procedures for finding outliers in high dimensional multivariate time series and
robust estimation methods for these data are reviewed. First, methods for searching for …

Robust fuzzy clustering of multivariate time series is addressed when the clustering purpose
is grouping together series generated from similar stochastic processes. Robustness to the …

A method for detecting outlier samples in a multivariate time series dataset is proposed. It is
assumed that an outlying series is characterized by having been generated from a different …

In this article, a fuzzy clustering model for multivariate time series based on the quantile
cross-spectral density and principal component analysis is extended by including: 1) a …

Abstract Distributed Ledger Technology (DLT) is highly applicable in various fields,
especially the supply chain in many sectors. Against limited empirical evidence, this paper …