Visual Analytics for Development and Evaluation of Order Selection Criteria for Autoregressive Processes

Order selection of autoregressive processes is an active research topic in time series analysis, and the development and evaluation of automatic order selection criteria remains a challenging task for domain experts. We propose a visual analytics approach, to guide the analysis and development of su...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1996. - 22(2016), 1 vom: 24. Jan., Seite 151-9
1. Verfasser: Löwe, Thomas (VerfasserIn)
Weitere Verfasser: Förster, Emmy-Charlotte, Albuquerque, Georgia, Kreiss, Jens-Peter, Magnor, Marcus
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2016
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article Research Support, Non-U.S. Gov't
Beschreibung
Zusammenfassung:Order selection of autoregressive processes is an active research topic in time series analysis, and the development and evaluation of automatic order selection criteria remains a challenging task for domain experts. We propose a visual analytics approach, to guide the analysis and development of such criteria. A flexible synthetic model generator-combined with specialized responsive visualizations-allows comprehensive interactive evaluation. Our fast framework allows feedback-driven development and fine-tuning of new order selection criteria in real-time. We demonstrate the applicability of our approach in three use-cases for two general as well as a real-world example
Beschreibung:Date Completed 05.02.2016
Date Revised 04.11.2015
published: Print
Citation Status PubMed-not-MEDLINE
ISSN:1941-0506
DOI:10.1109/TVCG.2015.2467612