Visual analytics for model selection in time series analysis.
Model selection in time series analysis is a challenging task for domain experts in many application areas such as epidemiology, economy, or environmental sciences. The methodology used for this task demands a close combination of human judgement and automated computation. However, statistical software tools do not adequately support this combination through interactive visual interfaces. We propose a Visual Analytics process to guide domain experts in this task. For this purpose, we developed the TiMoVA prototype that implements this process based on user stories and iterative expert feedback on user experience. The prototype was evaluated by usage scenarios with an example dataset from epidemiology and interviews with two external domain experts in statistics. The insights from the experts' feedback and the usage scenarios show that TiMoVA is able to support domain experts in model selection tasks through interactive visual interfaces with short feedback cycles.
Vienna University of Technology.
Markus
M
Bögl
Wolfgang
W
Aigner
Peter
P
Filzmoser
Tim
T
Lammarsch
Silvia
S
Miksch
Alexander
A
Rind
D000465:Algorithms; D003196:Computer Graphics; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003661:Decision Support Techniques; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface