Visual time series analysis

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

Standard

Visual time series analysis. / Fischer, Paul; Hilbert, Astrid.

Proceedings of COMPSTAT 2012: 20th International Conference on Computational Statistics. 2012. p. 225-234.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

Harvard

Fischer, P & Hilbert, A 2012, 'Visual time series analysis'. in Proceedings of COMPSTAT 2012: 20th International Conference on Computational Statistics. pp. 225-234.

APA

Fischer, P., & Hilbert, A. (2012). Visual time series analysis. In Proceedings of COMPSTAT 2012: 20th International Conference on Computational Statistics. (pp. 225-234)

CBE

Fischer P, Hilbert A. 2012. Visual time series analysis. In Proceedings of COMPSTAT 2012: 20th International Conference on Computational Statistics. pp. 225-234.

MLA

Fischer, Paul and Astrid Hilbert "Visual time series analysis". Proceedings of COMPSTAT 2012: 20th International Conference on Computational Statistics. 2012. 225-234.

Vancouver

Fischer P, Hilbert A. Visual time series analysis. In Proceedings of COMPSTAT 2012: 20th International Conference on Computational Statistics. 2012. p. 225-234.

Author

Fischer, Paul; Hilbert, Astrid / Visual time series analysis.

Proceedings of COMPSTAT 2012: 20th International Conference on Computational Statistics. 2012. p. 225-234.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

Bibtex

@inbook{54b7e2f808c04094a276e99bcd379161,
title = "Visual time series analysis",
keywords = "Computational Statistics, Times series analysis, Efficient algorithms",
author = "Paul Fischer and Astrid Hilbert",
year = "2012",
isbn = "978-90-73592-32-2",
pages = "225-234",
booktitle = "Proceedings of COMPSTAT 2012",

}

RIS

TY - GEN

T1 - Visual time series analysis

A1 - Fischer,Paul

A1 - Hilbert,Astrid

AU - Fischer,Paul

AU - Hilbert,Astrid

PY - 2012

Y1 - 2012

N2 - We introduce a platform which supplies an easy-to-handle, interactive, extendable, and fast analysis tool for time series analysis. In contrast to other software suits like Maple, Matlab, or R, which use a command-line-like interface and where the user has to memorize/look-up the appropriate commands, our application is select-and-click-driven. It allows to derive many different sequences of deviations for a given time series and to visualize them in different ways in order to judge their expressive power and to reuse the procedure found. For many transformations or model-ts, the user may choose between manual and automated parameter selection. The user can dene new transformations and add them to the system. The application contains efficient implementations of advanced and recent techniques for time series analysis including techniques related to extreme value analysis and filtering theory. It has been successfully applied to time series in economics, e.g. reinsurance, and to vibrational stress data for machinery. The software is web-deployed, but runs on the user's machine, allowing to process sensitive data locally without having to send it away. The software can be accessed under http://www.imm.dtu.dk/~paf/TSA/launch.html.

AB - We introduce a platform which supplies an easy-to-handle, interactive, extendable, and fast analysis tool for time series analysis. In contrast to other software suits like Maple, Matlab, or R, which use a command-line-like interface and where the user has to memorize/look-up the appropriate commands, our application is select-and-click-driven. It allows to derive many different sequences of deviations for a given time series and to visualize them in different ways in order to judge their expressive power and to reuse the procedure found. For many transformations or model-ts, the user may choose between manual and automated parameter selection. The user can dene new transformations and add them to the system. The application contains efficient implementations of advanced and recent techniques for time series analysis including techniques related to extreme value analysis and filtering theory. It has been successfully applied to time series in economics, e.g. reinsurance, and to vibrational stress data for machinery. The software is web-deployed, but runs on the user's machine, allowing to process sensitive data locally without having to send it away. The software can be accessed under http://www.imm.dtu.dk/~paf/TSA/launch.html.

KW - Computational Statistics

KW - Times series analysis

KW - Efficient algorithms

SN - 978-90-73592-32-2

BT - Proceedings of COMPSTAT 2012

T2 - Proceedings of COMPSTAT 2012

SP - 225

EP - 234

ER -