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 …

Unsupervised learning of time series data, also known as temporal clustering, is a
challenging problem in machine learning. This thesis presents a novel algorithm, Deep …

The statistical discrimination and clustering literature has studied the problem of identifying
similarities in time series data. Some studies use non-parametric approaches for splitting a …

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 …

The boom of Big Data has made the development of prediction algorithms more intelligent,
so the studies have gradually shifted from the traditional linear prediction algorithm (a typical …

The increasing mismatch between the demand and supply of power in Nigeria raises
concerns about the ability of this country to meet its vital energy security and sustainability …

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 …

In this paper we propose a clustering procedure aimed at grouping time series with an
association between extremely low values, measured by the lower tail dependence …

A new clustering method for time series is proposed, based on the full probability density of
the forecasts. First, a resampling method combined with a nonparametric kernel estimator …