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Matrix-based visual correlation analysis on large timeseries data

: Behrisch, Michael; Davey, James; Schreck, Tobias; Keim, Daniel A.; Kohlhammer, Jörn


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 : 14-19 October 2012, Seattle, Washington
New York, NY: IEEE, 2012
ISBN: 978-1-4673-4752-5
ISBN: 978-1-4673-4753-2
Conference on Visual Analytics Science and Technology (VAST) <2012, Seattle/Wash.>
Fraunhofer IGD ()
visual analytic; time series analysis; matrix representation

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.