On-The-Fly Approximation of Multivariate Total Variation Minimization

Frecon, Jordan; Pustelnik, Nelly; Abry, Patrice; Condat, Laurent

doi:10.1109/tsp.2016.2516962

Cited by 9 publications

(8 citation statements)

References 33 publications

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“…Indeed, for interpretation in macroeconomics, only the global dominant structure needs to be constant and not all pair-wise correlations. Hence, we first extract a global structure by extracting features from the correlation-based graphs (as proposed in econophysics) before using multivariate time series segmentations, leveraging here the methods from [8].…”

Section: Data-driven Based Segmentationmentioning

confidence: 99%

“…In a third step (illustrated in Fig. 2), a multivariate piece-wise constant signal denoising procedure, based on functional optimization [8], is applied to these topological index time series to detect change points in the structure of the graph. The whole procedure allows to identify globalization eras characterized by locally stationary network structures.…”

Section: Contributions and Outlinementioning

confidence: 99%

“…From the graph-based representation of the evolving dependences between equity markets, a graph-based segmentation procedure is developed, which puts the focus on the global and topological structure of the graph, rather than on pairwise relations. This procedure will leverage the multivariate segmentation methods studied in [8], applied simultaneously to time-dependant features extracted from the graph representations of the dependences between equity markets.…”

Section: Introductionmentioning

confidence: 99%

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Graph-based era segmentation of international financial integration

Bastidon

Parent

Jensen

et al. 2020

Physica A: Statistical Mechanics and its Applications

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Assessing world-wide financial integration constitutes a recurrent challenge in macroeconometrics, often addressed by visual inspections searching for data patterns. Econophysics literature enables us to build complementary, data-driven measures of financial integration using graphs. The present contribution investigates the potential and interests of a novel 3-step approach that combines several state-of-the-art procedures to i) compute graph-based representations of the multivariate dependence structure of asset prices time series representing the financial states of 32 countries world-wide ; ii) compute time series of 5 graph-based indices that characterize the time evolution of the topologies of the graph; iii) segment these time evolutions in piece-wise constant eras, using an optimization framework constructed on a multivariate multi-norm total variation penalized functional. The method shows first that it is possible to find endogenous stable eras of world-wide financial integration. Then, our results suggest that the most relevant globalization eras would be based on the historical patterns of global capital flows, while the major regulatory events of the 1970s would only appear as a cause of sub-segmentation.

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Section: Data-driven Based Segmentationmentioning

confidence: 99%

Section: Contributions and Outlinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Graph-based era segmentation of international financial integration

Bastidon

Parent

Jensen

et al. 2020

Physica A: Statistical Mechanics and its Applications

Self Cite

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“…This can be used to perform CD in the lower dimensional space [1]. Other methods based on, e.g., total-variation [14] and low-rank and sparse decomposition [15] can also be considered to perform CD. However, reconciling a low computational complexity with flexibility to incorporate a priori knowledge about the nature of the changes can be difficult in these techniques.…”

Section: Introductionmentioning

confidence: 99%

Online Graph-Based Change Point Detection in Multiband Image Sequences

Borsoi¹,

Richard²,

André³

et al. 2020

Preprint

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The automatic detection of changes or anomalies between multispectral and hyperspectral images collected at different time instants is an active and challenging research topic. To effectively perform change-point detection in multitemporal images, it is important to devise techniques that are computationally efficient for processing large datasets, and that do not require knowledge about the nature of the changes. In this paper, we introduce a novel online framework for detecting changes in multitemporal remote sensing images. Acting on neighboring spectra as adjacent vertices in a graph, this algorithm focuses on anomalies concurrently activating groups of vertices corresponding to compact, well-connected and spectrally homogeneous image regions. It fully benefits from recent advances in graph signal processing to exploit the characteristics of the data that lie on irregular supports. Moreover, the graph is estimated directly from the images using superpixel decomposition algorithms. The learning algorithm is scalable in the sense that it is efficient and spatially distributed. Experiments illustrate the detection and localization performance of the method.

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“…Finding consistent segments in a signal/time series is an old problem, referred to as the change-point problem in the field of statistics. Most resources in the literature cope with finding change points in a noisy, piecewise-constant signal, which is either singlevariate [23] or multivariate [2,13] (the latter is the case where most of the signals or all of them change simultaneously). In a more general context of detecting sudden changes in the underlying, latent parameters we mention works [37] and [1].…”

Section: Introductionmentioning

confidence: 99%