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Exploring time series with visualization analysis

Grant number: 18/10652-9
Support Opportunities:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): October 01, 2018
Effective date (End): September 30, 2019
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computational Mathematics
Principal Investigator:Rodrigo Fernandes de Mello
Grantee:Lucas de Carvalho Pagliosa
Supervisor: Alexandru-Cristian Telea
Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Research place: University of Groningen, Netherlands  
Associated to the scholarship:15/22406-4 - Optimization of the reconstruction of phase spaces for time series, BP.DR


Data analysis has been motivating new methods to find patterns, clusters and outliers in different scenarios and conditions. In the scope of data evolving along time, also referred to as time series or data streams, solutions derived from areas such as Dynamical Systems and Statistics (DSS) have gained special attention in the literature. However, the DSS framework is usually not applied/combined with other areas in the given context. Based on this drawback, we propose an exploratory method with supporting techniques to analyze time series combining DSS approaches with Visualization metaphors to answer typical questions related to time-dependent data in a more effective and efficient way. More than having a user-friendly application providing interactive exploration, we understand Visualization adds value to DSS by revealing additional data information. With our proposed method, namely Visual DSS (VDSS), one could determine a series nature (deterministic, chaotic, stationary, etc.), forecast never seen observations, and proceed with classification. More precisely, this project has three lines of research. First, we intend to improve how one can visually explore time series similarities and attributes. In order to do that, we consider an hierarchical-bundle metaphor based on the Cross-Recurrence Quantification Analysis. Second, we correlate phase-spaces and time-series attributes through the usage of Multidimensional Projections, RadViz and Parallel Coordinates metaphors. Finally, we investigate how much dimensionality reduction impacts forecasting accuracy after applying Takens' embedding theorem.

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
PAGLIOSA, LUCAS DE CARVALHO; TELEA, ALEXANDRU C.. RadViz++: Improvements on Radial-Based Visualizations. INFORMATICS-BASEL, v. 6, n. 2, . (18/10652-9)

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