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Matrix-Based Visual Correlation Analysis on Large Timeseries Data

Behrisch, Michael and Davey, James and Schreck, Tobias and Keim, Daniel A. and Kohlhammer, Jörn (2012):
Matrix-Based Visual Correlation Analysis on Large Timeseries Data.
IEEE Press, New York, In: IEEE Conference on Visual Analytics Science and Technology 2012. Proceedings, DOI: 10.1109/VAST.2012.6400549, [Conference or Workshop Item]

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 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.

Item Type: Conference or Workshop Item
Erschienen: 2012
Creators: Behrisch, Michael and Davey, James and Schreck, Tobias and Keim, Daniel A. and Kohlhammer, Jörn
Title: Matrix-Based Visual Correlation Analysis on Large Timeseries Data
Language: English
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 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.

Publisher: IEEE Press, New York
Uncontrolled Keywords: Business Field: Visual decision support, Research Area: Generalized digital documents, Visual analytics, Time series analysis, Matrix representation
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: IEEE Conference on Visual Analytics Science and Technology 2012. Proceedings
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1109/VAST.2012.6400549
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