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Clustering and classification of time series using topological data analysis with applications to financePersistent homologyTime delay embeddingTakens theoremRandom forestSelf organizing mapsIn this paper, we propose new methods for time series classification and clustering. These methods are based on ...
Topological data analysis is interested in problems relating to nonlinear systems, large scale data and development of more accurate models, that contribute to a high level research. In the study of time-series data we can identify problems that focus aspects of that nature, with applications to ...
Biscio, C., Moller, J.: The accumulated persistence function, a new useful functional summary statistic for topological data analysis, with a view to brain artery trees and spatial point process applications. (2016). arXiv:1611.00630C. Biscio and J. Moller. The accumulated persistence function...
This article explores the applications of Topological Data Analysis (TDA) in the finance field, especially addressing the primordial problem of asset allocation. Firstly, we build a rationale on why TDA can be a better alternative to traditional risk indicators such as standard deviation using real ...
LNCS 12929Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data4th International Workshop, iMIMIC 2021and 1st International Workshop, TDA4MedicalData 2021Held in Conjunction with MICCAI 2021Strasbourg, France, September 27,...
V-Mapper: topological data analysis for high-dimensional data with velocity Mapper, a topological data analysis method for high-dimensional data, represents a topological structure as a simplicial complex or graph based on the nerv... Y Imoto,Y Hiraoka - 《Nonlinear Theory & Its Applications Ieice...
The method combines the asymptotic analysis of PDE's with an application of neural networks. The asymptotic analysis is performed in singularly perturbed geometrical domains with the imperfections in form of small voids and results in the form of the so-called topological derivatives of observation ...
with uniform mean-level activation across all RSNs. Figure5Cshows average distribution of S.D. values, over ten MSC participants, for hubs (blue) and other nodes (orange). As evident, the hubs had significantly lower S.D. values than non-hub nodes (for both splits of the data; odd: ...
Accelerated materials development with machine learning (ML) assisted screening and high throughput experimentation for new photovoltaic materials holds the key to addressing our grand energy challenges. Data-driven ML is envisaged as a decisive enabler