Cycle Plot Revisited: Multivariate Outlier Detection Using a Distance‐Based Abstraction

The cycle plot is an established and effective visualization technique for identifying and comprehending patterns in periodic time series, like trends and seasonal cycles. It also allows to visually identify and contextualize extreme values and outliers from a different perspective. Unfortunately, i...

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Vydané v:	Computer graphics forum Ročník 36; číslo 3; s. 227 - 238
Hlavní autori:	Bögl, M., Filzmoser, P., Gschwandtner, T., Lammarsch, T., Leite, R. A., Miksch, S., Rind, A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Blackwell Publishing Ltd 01.06.2017
Predmet:	Analytics Categories and Subject Descriptors (according to ACM CCS) Data analysis Exploration Extreme values Information Interfaces and Presentation [H.5.2]: User Interfaces—Graphical user interfaces Mathematics of Computing [G.3]: Probability and Statistics—Time Series Analysis Multivariate analysis Outliers (statistics) Time series
ISSN:	0167-7055, 1467-8659
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Abstract	The cycle plot is an established and effective visualization technique for identifying and comprehending patterns in periodic time series, like trends and seasonal cycles. It also allows to visually identify and contextualize extreme values and outliers from a different perspective. Unfortunately, it is limited to univariate data. For multivariate time series, patterns that exist across several dimensions are much harder or impossible to explore. We propose a modified cycle plot using a distance‐based ion (Mahalanobis distance) to reduce multiple dimensions to one overview dimension and retain a representation similar to the original. Utilizing this distance‐based cycle plot in an interactive exploration environment, we enhance the Visual Analytics capacity of cycle plots for multivariate outlier detection. To enable interactive exploration and interpretation of outliers, we employ coordinated multiple views that juxtapose a distance‐based cycle plot with Cleveland's original cycle plots of the underlying dimensions. With our approach it is possible to judge the outlyingness regarding the seasonal cycle in multivariate periodic time series.
AbstractList	The cycle plot is an established and effective visualization technique for identifying and comprehending patterns in periodic time series, like trends and seasonal cycles. It also allows to visually identify and contextualize extreme values and outliers from a different perspective. Unfortunately, it is limited to univariate data. For multivariate time series, patterns that exist across several dimensions are much harder or impossible to explore. We propose a modified cycle plot using a distance-based abstraction (Mahalanobis distance) to reduce multiple dimensions to one overview dimension and retain a representation similar to the original. Utilizing this distance-based cycle plot in an interactive exploration environment, we enhance the Visual Analytics capacity of cycle plots for multivariate outlier detection. To enable interactive exploration and interpretation of outliers, we employ coordinated multiple views that juxtapose a distance-based cycle plot with Cleveland's original cycle plots of the underlying dimensions. With our approach it is possible to judge the outlyingness regarding the seasonal cycle in multivariate periodic time series. The cycle plot is an established and effective visualization technique for identifying and comprehending patterns in periodic time series, like trends and seasonal cycles. It also allows to visually identify and contextualize extreme values and outliers from a different perspective. Unfortunately, it is limited to univariate data. For multivariate time series, patterns that exist across several dimensions are much harder or impossible to explore. We propose a modified cycle plot using a distance‐based ion (Mahalanobis distance) to reduce multiple dimensions to one overview dimension and retain a representation similar to the original. Utilizing this distance‐based cycle plot in an interactive exploration environment, we enhance the Visual Analytics capacity of cycle plots for multivariate outlier detection. To enable interactive exploration and interpretation of outliers, we employ coordinated multiple views that juxtapose a distance‐based cycle plot with Cleveland's original cycle plots of the underlying dimensions. With our approach it is possible to judge the outlyingness regarding the seasonal cycle in multivariate periodic time series.
Author	Gschwandtner, T. Rind, A. Miksch, S. Lammarsch, T. Bögl, M. Leite, R. A. Filzmoser, P.
Author_xml	– sequence: 1 givenname: M. surname: Bögl fullname: Bögl, M. organization: Vienna University of Technology – sequence: 2 givenname: P. surname: Filzmoser fullname: Filzmoser, P. organization: Vienna University of Technology – sequence: 3 givenname: T. surname: Gschwandtner fullname: Gschwandtner, T. organization: Vienna University of Technology – sequence: 4 givenname: T. surname: Lammarsch fullname: Lammarsch, T. organization: Vienna University of Technology – sequence: 5 givenname: R. A. surname: Leite fullname: Leite, R. A. organization: Vienna University of Technology – sequence: 6 givenname: S. surname: Miksch fullname: Miksch, S. organization: Vienna University of Technology – sequence: 7 givenname: A. surname: Rind fullname: Rind, A. organization: St. Pölten University of Applied Sciences
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SubjectTerms	Analytics Categories and Subject Descriptors (according to ACM CCS) Data analysis Exploration Extreme values Information Interfaces and Presentation [H.5.2]: User Interfaces—Graphical user interfaces Mathematics of Computing [G.3]: Probability and Statistics—Time Series Analysis Multivariate analysis Outliers (statistics) Time series
Title	Cycle Plot Revisited: Multivariate Outlier Detection Using a Distance‐Based Abstraction
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