multidimensional scaling visualization using parametric similarity indices

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ID: 220368
2015
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Abstract
In this paper, we apply multidimensional scaling (MDS) and parametric similarity indices (PSI) in the analysis of complex systems (CS). Each CS is viewed as a dynamical system, exhibiting an output time-series to be interpreted as a manifestation of its behavior. We start by adopting a sliding window to sample the original data into several consecutive time periods. Second, we define a given PSI for tracking pieces of data. We then compare the windows for different values of the parameter, and we generate the corresponding MDS maps of ‘points’. Third, we use Procrustes analysis to linearly transform the MDS charts for maximum superposition and to build a globalMDS map of “shapes”. This final plot captures the time evolution of the phenomena and is sensitive to the PSI adopted. The generalized correlation, theMinkowski distance and four entropy-based indices are tested. The proposed approach is applied to the Dow Jones Industrial Average stock market index and the Europe Brent Spot Price FOB time-series.
Reference Key
machado2015entropymultidimensional Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;J. A. Tenreiro Machado;António M. Lopes;Alexandra M. Galhano
Journal European journal of medicinal chemistry
Year 2015
DOI
10.3390/e17041775
URL
http://www.mdpi.com/1099-4300/17/4/1775
Keywords
clustering complex systems multidimensional scaling visualizationscienceastrophysicsphysics

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