Correlation-based hierarchical clustering of time series with spatial constraints

Benevento, Alessia; Durante, Fabrizio

doi:10.1016/j.spasta.2023.100797

Cited by 5 publications

(1 citation statement)

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“…In the copula-based framework, such an approach has been adopted, for instance, in [27,28], which considers the temporal matrix induced by the (pairwise) copula parameters. In [29], instead, the correlation-based dissimilarity matrix is transformed by means of the spatial information, taking into account the intrinsic geometry of the space of correlation matrices. According to [30], all these methods have the tendency to produce a smooth dissimilarity matrix without, however, reinforcing the spatial contiguity between the resulting clusters.…”

Section: Introductionmentioning

confidence: 99%

Wasserstein Dissimilarity for Copula-Based Clustering of Time Series with Spatial Information

Benevento,

Durante

2023

Mathematics

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The clustering of time series with geo-referenced data requires a suitable dissimilarity matrix interpreting the comovements of the time series and taking into account the spatial constraints. In this paper, we propose a new way to compute the dissimilarity matrix, merging both types of information, which leverages on the Wasserstein distance. We then make a quasi-Gaussian assumption that yields more convenient formulas in terms of the joint correlation matrix. The method is illustrated in a case study involving climatological data.

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Section: Introductionmentioning

confidence: 99%

Wasserstein Dissimilarity for Copula-Based Clustering of Time Series with Spatial Information

Benevento,

Durante

2023

Mathematics

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Tail-dependence clustering of time series with spatial constraints

Benevento,

Durante,

Pappadà

2024

Environ Ecol Stat

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We introduce a clustering method for time series based on tail dependence. Such a method also considers spatial constraints by means of a suitable procedure merging temporal and spatial dependence via extreme-value copulas. The cluster composition depends on the choice of the hyper-parameter $$\alpha \in (0,1)$$ α ∈ ( 0 , 1 ) used to calibrate the contribution of the spatial dependence to the overall dissimilarity. A novel heuristic approach to select $$\alpha$$ α based on a suitable connectedness index associated to each cluster of the partition is proposed.

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Renewable energy communities in rural areas: A comprehensive overview of current development, challenges, and emerging trends

Shi,

Liang,

Pezzuolo

2024

Journal of Cleaner Production

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Correlation-based hierarchical clustering of time series with spatial constraints

Cited by 5 publications

References 37 publications

Wasserstein Dissimilarity for Copula-Based Clustering of Time Series with Spatial Information

Wasserstein Dissimilarity for Copula-Based Clustering of Time Series with Spatial Information

Tail-dependence clustering of time series with spatial constraints

Renewable energy communities in rural areas: A comprehensive overview of current development, challenges, and emerging trends

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