In the last 5 years there have been a large number of new time series classification
algorithms proposed in the literature. These algorithms have been evaluated on subsets of …

Time series clustering has been shown effective in providing useful information in various
domains. There seems to be an increased interest in time series clustering as part of the …

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 monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …

This is the first book to take a truly comprehensive look at clustering. It begins with an
introduction to cluster analysis and goes on to explore: proximity measures; hierarchical …

Time series are ubiquitous, and a measure to assess their similarity is a core part of many
computational systems. In particular, the similarity measure is the most essential ingredient …

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

In this paper, we propose a technique for time series clustering using community detection in
complex networks. Firstly, we present a method to transform a set of time series into a …

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 …