Matrix-Based Visual Correlation Analysis on Large Timeseries Data

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BEHRISCH, Michael, James DAVEY, Tobias SCHRECK, Daniel KEIM, Jörn KOHLHAMMER, 2012. Matrix-Based Visual Correlation Analysis on Large Timeseries Data. 2012 IEEE Conference on Visual Analytics Science and Technology (VAST). Seattle, WA, USA, 14. Okt 2012 - 19. Okt 2012. In: 2012 IEEE Conference on Visual Analytics Science and Technology (VAST). 2012 IEEE Conference on Visual Analytics Science and Technology (VAST). Seattle, WA, USA, 14. Okt 2012 - 19. Okt 2012. IEEE, pp. 209-210. ISBN 978-1-4673-4752-5. Available under: doi: 10.1109/VAST.2012.6400549

@inproceedings{Behrisch2012-10Matri-22530, title={Matrix-Based Visual Correlation Analysis on Large Timeseries Data}, year={2012}, doi={10.1109/VAST.2012.6400549}, isbn={978-1-4673-4752-5}, publisher={IEEE}, booktitle={2012 IEEE Conference on Visual Analytics Science and Technology (VAST)}, pages={209--210}, author={Behrisch, Michael and Davey, James and Schreck, Tobias and Keim, Daniel and Kohlhammer, Jörn} }

Matrix-Based Visual Correlation Analysis on Large Timeseries Data Schreck, Tobias deposit-license IEEE Conference on Visual Analytics Science & Technology 2012 : Seattle, Washington, USA, 14 - 19 October 2012 ; Proceedings / Giuseppe Santucci and Matthew Ward (eds.). - Piscataway, NJ : IEEE, 2012, S. 209-210. - ISBN 978-1-4673-4753-2 2012-10 Davey, James Behrisch, Michael eng Kohlhammer, Jörn 2013-03-25T10:52:39Z Kohlhammer, Jörn Behrisch, Michael Schreck, Tobias Davey, James 2013-03-25T10:52:39Z 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 large-scale 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 real-world data set, showing the impact of local weather conditions on the efficiency of photovoltaic power plants. Keim, Daniel Keim, Daniel

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