Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Matrixbased visual correlation analysis on large timeseries data
 Santucci, G. ; IEEE Computer Society; Institute of Electrical and Electronics Engineers IEEE, Technical Committee on Visualization and Graphics TCVG: IEEE Conference on Visual Analytics Science & Technology, VAST 2012. Proceedings : 1419 October 2012, Seattle, Washington New York, NY: IEEE, 2012 ISBN: 9781467347525 ISBN: 9781467347532 S.209210 
 Conference on Visual Analytics Science and Technology (VAST) <2012, Seattle/Wash.> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer IGD () 
 visual analytic; time series analysis; matrix representation 
Abstract
In recent years, the quantity of time series data generated in a wide variety of domains grown consistently. Thus, it is difficult for analysts to process and understand this overwhelming amount of data. In the specific case of time series data another problem arises: time series can be highly interrelated. This problem becomes even more challenging when a set of parameters influences the progression of a time series. However, while most visual analysis techniques support the analysis of short time periods, e.g. one day or one week, they fail to visualize largescale time series, ranging over one year or more. In our approach we present a time series matrix visualization that tackles this problem. Its primary advantages are that it scales to a large number of time series with different start and end points and allows for the visual comparison / correlation analysis of a set of influencing factors. To evaluate our approach, we applied our technique to a realworld data set, showing the impact of local weather conditions on the efficiency of photovoltaic power plants.