title: Visual Analytics for Development and Evaluation of Order Selection Criteria for Autoregressive Processes.
abstract: 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.
authors.forename: Thomas
authors.initials: T
authors.lastname: Löwe
authors.forename: Emmy-Charlotte
authors.initials: EC
authors.lastname: Förster
authors.forename: Georgia
authors.initials: G
authors.lastname: Albuquerque
authors.forename: Jens-Peter
authors.initials: JP
authors.lastname: Kreiss
authors.forename: Marcus
authors.initials: M
authors.lastname: Magnor