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PROJECT TITLE :

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

ABSTRACT:

Order choice of autoregressive processes is a vigorous research topic in time series analysis, and the development and analysis of automatic order choice criteria remains a difficult task for domain specialists. We propose a visible analytics approach, to guide the analysis and development of such criteria. A flexible synthetic model generator—combined with specialised responsive visualizations—allows comprehensive interactive analysis. Our fast framework allows feedback-driven development and fine-tuning of latest order selection criteria in real-time. We demonstrate the applicability of our approach in 3 use-cases for 2 general with a true-world example.


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