Visual time series analysis

Paul Fischer, Astrid Hilbert

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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    Abstract

    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.
    Original languageEnglish
    Title of host publicationProceedings of COMPSTAT 2012 : 20th International Conference on Computational Statistics
    Publication date2012
    Pages225-234
    ISBN (Print)978-90-73592-32-2
    Publication statusPublished - 2012
    Event20th International Conference on Computational Statistics (COMPSTAT 2012) - Limassol, Cyprus
    Duration: 27 Jul 201231 Aug 2012
    http://www.compstat2012.org/

    Conference

    Conference20th International Conference on Computational Statistics (COMPSTAT 2012)
    CountryCyprus
    CityLimassol
    Period27/07/201231/08/2012
    Internet address

    Keywords

    • Computational Statistics
    • Times series analysis
    • Efficient algorithms

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