Timeseer: Detecting interesting distributions in multiple time series data

Tuan Nhon Dang; Leland Wilkinson

doi:10.1145/2397696.2397703

Timeseer: Detecting interesting distributions in multiple time series data

Tuan Nhon Dang, Leland Wilkinson

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Widespread interest in features and trends in time series has generated a need for interactive tools that support discovering unusual events in time series. In this paper, we introduce an application (TimeSeer) for guiding interactive exploration through high-dimensional data. Our application is designed to handle the types of doubly-multivariate data series by working directly on noteworthy features such as density, skewness, shape, outliers, and texture.

Original language	English
Title of host publication	VINCI 2012 - 5th International Symposium on Visual Information Communication and Interaction
Pages	43-51
Number of pages	9
DOIs	https://doi.org/10.1145/2397696.2397703
State	Published - 2012
Event	5th International Symposium on Visual Information Communication and Interaction, VINCI 2012 - Hangzhou, China Duration: Sep 27 2012 → Sep 28 2012

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	5th International Symposium on Visual Information Communication and Interaction, VINCI 2012
Country/Territory	China
City	Hangzhou
Period	09/27/12 → 09/28/12

Keywords

scagnostics
scatterplot
time serie visualization

Access to Document

10.1145/2397696.2397703

Cite this

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title = "Timeseer: Detecting interesting distributions in multiple time series data",

abstract = "Widespread interest in features and trends in time series has generated a need for interactive tools that support discovering unusual events in time series. In this paper, we introduce an application (TimeSeer) for guiding interactive exploration through high-dimensional data. Our application is designed to handle the types of doubly-multivariate data series by working directly on noteworthy features such as density, skewness, shape, outliers, and texture.",

keywords = "scagnostics, scatterplot, time serie visualization",

author = "Dang, {Tuan Nhon} and Leland Wilkinson",

year = "2012",

doi = "10.1145/2397696.2397703",

language = "English",

isbn = "9781450317825",

series = "ACM International Conference Proceeding Series",

pages = "43--51",

booktitle = "VINCI 2012 - 5th International Symposium on Visual Information Communication and Interaction",

note = "5th International Symposium on Visual Information Communication and Interaction, VINCI 2012 ; Conference date: 27-09-2012 Through 28-09-2012",

}

Dang, TN & Wilkinson, L 2012, Timeseer: Detecting interesting distributions in multiple time series data. in VINCI 2012 - 5th International Symposium on Visual Information Communication and Interaction. ACM International Conference Proceeding Series, pp. 43-51, 5th International Symposium on Visual Information Communication and Interaction, VINCI 2012, Hangzhou, China, 09/27/12. https://doi.org/10.1145/2397696.2397703

Timeseer: Detecting interesting distributions in multiple time series data. / Dang, Tuan Nhon; Wilkinson, Leland.
VINCI 2012 - 5th International Symposium on Visual Information Communication and Interaction. 2012. p. 43-51 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Wilkinson, Leland

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N2 - Widespread interest in features and trends in time series has generated a need for interactive tools that support discovering unusual events in time series. In this paper, we introduce an application (TimeSeer) for guiding interactive exploration through high-dimensional data. Our application is designed to handle the types of doubly-multivariate data series by working directly on noteworthy features such as density, skewness, shape, outliers, and texture.

AB - Widespread interest in features and trends in time series has generated a need for interactive tools that support discovering unusual events in time series. In this paper, we introduce an application (TimeSeer) for guiding interactive exploration through high-dimensional data. Our application is designed to handle the types of doubly-multivariate data series by working directly on noteworthy features such as density, skewness, shape, outliers, and texture.

KW - scagnostics

KW - scatterplot

KW - time serie visualization

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M3 - Conference contribution

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SN - 9781450317825

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BT - VINCI 2012 - 5th International Symposium on Visual Information Communication and Interaction

Y2 - 27 September 2012 through 28 September 2012

ER -

Timeseer: Detecting interesting distributions in multiple time series data

Abstract

Publication series

Conference

Keywords

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