Type of Publication:  Contribution to a conference collection 
URI (citable link):  http://nbnresolving.de/urn:nbn:de:bsz:352225306 
Author:  Behrisch, Michael; Davey, James; Schreck, Tobias; Keim, Daniel; Kohlhammer, Jörn 
Year of publication:  2012 
Conference:  2012 IEEE Conference on Visual Analytics Science and Technology (VAST), Oct 14, 2012  Oct 19, 2012, Seattle, WA, USA 
Published in:  2012 IEEE Conference on Visual Analytics Science and Technology (VAST).  IEEE, 2012.  pp. 209210.  ISBN 9781467347525 
DOI (citable link):  https://dx.doi.org/10.1109/VAST.2012.6400549 
Summary: 
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.

Subject (DDC):  004 Computer Science 
Link to License:  In Copyright 
Bibliography of Konstanz:  Yes 
BEHRISCH, Michael, James DAVEY, Tobias SCHRECK, Daniel KEIM, Jörn KOHLHAMMER, 2012. MatrixBased Visual Correlation Analysis on Large Timeseries Data. 2012 IEEE Conference on Visual Analytics Science and Technology (VAST). Seattle, WA, USA, Oct 14, 2012  Oct 19, 2012. In: 2012 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, pp. 209210. ISBN 9781467347525. Available under: doi: 10.1109/VAST.2012.6400549
@inproceedings{Behrisch201210Matri22530, title={MatrixBased Visual Correlation Analysis on Large Timeseries Data}, year={2012}, doi={10.1109/VAST.2012.6400549}, isbn={9781467347525}, publisher={IEEE}, booktitle={2012 IEEE Conference on Visual Analytics Science and Technology (VAST)}, pages={209210}, author={Behrisch, Michael and Davey, James and Schreck, Tobias and Keim, Daniel and Kohlhammer, Jörn} }
Behrisch_225306.pdf  295 