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

Time series clustering is often applied to pattern recognition and also as the basis of the
tasks in the field of time series data mining including dimensionality reduction, feature …

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 …

Clustering of multivariate time series is a central problem in data mining with applications in
many fields. Frequently, the clustering target is to identify groups of series generated by the …

Nowadays, great quantities of data are produced by a large and diverse family of sensors
(eg, remote sensors, biochemical sensors, wearable devices), which typically measure …

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 …

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

In analysing ECG data, the main aim is to differentiate between the signal patterns of healthy
subjects and those of individuals with specific heart conditions. We propose an approach for …

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 …

Timeseries representation underpin our ability to understand and predict the change of
natural system. Series are often predicated on our choice of highly redundant factors, and in …