A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree
Rodriguez Urquiaga, Roberto
Cuadros Valdivia, Ana María
Alfonte Zapana, Reynaldo
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High-dimensional time series analysis through visual techniques poses many challenges due to the visualization solutions proposed until now for exploratory tasks are not well-oriented to high volume of data. When the data sets grow large, the visual alternatives do not allow for a good association between similar time series. With the aim to increase more alternatives, we introduce a visual analytic approach based on Neighbor-Joining similarity tree. The proposed approach internally consists of five time series dimension reduction techniques widely used, two well-known similarity measures and interaction mechanisms to do exploratory analysis of high-dimensional time series data interactively.