Contributed Session 14: Time Series Analysis

When clustering time series, relevant information resides in their pairwise associations. Therefore, a measure of dissimilarity among time series is required. When the phenomenon of interest exhibits extreme dependence, it is possible to base such metrics on tail dependence coefficients. Identifying the best measure is far from being trivial and largely impacts the resulting partition. In this contribution, we propose a novel approach, which accumulates evidence from a multiplicity of partitions obtained from several dissimilarities, potentially resulting in more robust final clusters. We finally illustrate the proposed approach by applying it to the analysis of financial time series, where the emphasis is on possible lower tail dependence.

This paper presents a novel approach to analyzing the Italian construction sector by modeling companies from each province through a correlation network of economic and financial indicators. By leveraging whole graph embedding techniques and cluster analysis, the study reveals structural similarities and differences across provinces. The findings provide insights into the spatial dimensions of the market, highlighting how territorial characteristics influence the economic and financial performance of construction companies.

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