evaluation-paper

Supporting material and website for the paper "Anomaly Detection in Time Series: A Comprehensive Evaluation"

https://github.com/timeeval/evaluation-paper

Science Score: 44.0%

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CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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Scientific vocabulary similarity
Low similarity (4.9%) to scientific vocabulary

Keywords

evaluation paper time-series time-series-anomaly-detection

Last synced: 6 months ago · JSON representation ·

Repository

Supporting material and website for the paper "Anomaly Detection in Time Series: A Comprehensive Evaluation"

Basic Info

Host: GitHub
Owner: TimeEval
License: mit
Default Branch: main
Homepage: https://timeeval.github.io/evaluation-paper/
Size: 14.7 MB

Statistics

Stars: 78
Watchers: 6
Forks: 8
Open Issues: 0
Releases: 0

Topics

evaluation paper time-series time-series-anomaly-detection

Created about 4 years ago · Last pushed almost 2 years ago

Metadata Files

Readme License Citation

README.md

Anomaly Detection in Time Series: A Comprehensive Evaluation

Go to the website for further information.

Time series anomaly detection experimental evaluation paper supporting material and website.

Related repositories

Time series anomaly generator GutenTAG
Evaluation tool TimeEval
Algorithm source code (and necessary docker images): TimeEval-algorithms

For the datasets, go to the datasets page.

Owner

Name: TimeEval
Login: TimeEval
Kind: organization
Location: Germany

Website: https://timeeval.readthedocs.io
Repositories: 1
Profile: https://github.com/TimeEval

Time series anomaly detection tools from the HPI Information Systems group

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use the results of this evaluation, please cite it as below."
authors:
  - family-names: Schmidl
    given-names: Sebastian
    orcid: https://orcid.org/0000-0002-6597-9809
  - family-names: Wenig
    given-names: Phillip
    orcid: https://orcid.org/0000-0002-8942-4322
title: "Anomaly Detection in Time Series: A Comprehensive Evaluation"
date-released: 2022
url: "https://hpi.de/naumann/s/time-series-anomaly-detection-evaluation"
preferred-citation:
  type: article
  authors:
    - family-names: Schmidl
      given-names: Sebastian
      orcid: https://orcid.org/0000-0002-6597-9809
    - family-names: Wenig
      given-names: Phillip
      orcid: https://orcid.org/0000-0002-8942-4322
    - family-names: Papenbrock
      given-names: Thorsten
      orcid: https://orcid.org/0000-0002-4019-8221
  doi: 10.14778/3538598.3538602
  journal: "Proceedings of the VLDB Endowment (PVLDB)"
  title: "Anomaly Detection in Time Series: A Comprehensive Evaluation"
  issue: 9
  volume: 15
  year: 2022
  start: 1779
  end: 1797

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