Matrix-Based Visual Correlation Analysis on Large Timeseries Data

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

Matrix-Based Visual Correlation Analysis on Large Timeseries Data

Dateien

Behrisch_225306.pdfGröße: 1.59 MBDownloads: 387

Datum

2012

Autor:innen

Behrisch, Michael

Davey, James

Schreck, Tobias

Keim, Daniel A.

Kohlhammer, Jörn

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

2012 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 2012, pp. 209-210. ISBN 978-1-4673-4752-5. Available under: doi: 10.1109/VAST.2012.6400549

Zusammenfassung

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.

Fachgebiet (DDC)

004 Informatik

Konferenz

2012 IEEE Conference on Visual Analytics Science and Technology (VAST), 14. Okt. 2012 - 19. Okt. 2012, Seattle, WA, USA

Zitieren

ISO 690

BEHRISCH, Michael, James DAVEY, Tobias SCHRECK, Daniel A. 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). IEEE, 2012, pp. 209-210. ISBN 978-1-4673-4752-5. Available under: doi: 10.1109/VAST.2012.6400549

BibTex

@inproceedings{Behrisch2012-10Matri-22530,
  year={2012},
  doi={10.1109/VAST.2012.6400549},
  title={Matrix-Based Visual Correlation Analysis on Large Timeseries Data},
  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 A. and Kohlhammer, Jörn}
}

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Universitätsbibliographie

Ja