loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Erick L. Trentini ; Ticiana L. Coelho da Silva ; Leopoldo Melo Junior and Jose F. de Macêdo

Affiliation: Insight Data Science Lab, Fortaleza, Brazil

Keyword(s): Time Series, Anomaly Detection, Ensemble.

Abstract: Time-series anomalies detection is a fast-growing area of study, due to the exponential growth of new data produced by sensors in many different contexts as the Internet of Things (IOT). Many predictive models have been proposed, and they provide promising results in differentiating normal and anomalous points in a time-series. In this paper, we aim to find and combine the best models on detecting anomalous time series, so that their different strategies or parameters can contribute to the time series analysis. We propose TSPME-AD (stands for Time Series Prediction Model Ensemble for Anomaly Detection). TSPME-AD is a model-centered based ensemble that trains some of the state-of-the-art predictive models with different hyper-parameters and combines their anomaly scores with a weighted function. The efficacy of our proposal was demonstrated in two real-world time-series datasets, power demand, and electrocardiogram.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.129.23.30

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Trentini, E.; Coelho da Silva, T.; Melo Junior, L. and F. de Macêdo, J. (2020). Model-centered Ensemble for Anomaly Detection in Time Series. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-395-7; ISSN 2184-433X, SciTePress, pages 700-707. DOI: 10.5220/0008985507000707

@conference{icaart20,
author={Erick L. Trentini. and Ticiana L. {Coelho da Silva}. and Leopoldo {Melo Junior}. and Jose {F. de Macêdo}.},
title={Model-centered Ensemble for Anomaly Detection in Time Series},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2020},
pages={700-707},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008985507000707},
isbn={978-989-758-395-7},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Model-centered Ensemble for Anomaly Detection in Time Series
SN - 978-989-758-395-7
IS - 2184-433X
AU - Trentini, E.
AU - Coelho da Silva, T.
AU - Melo Junior, L.
AU - F. de Macêdo, J.
PY - 2020
SP - 700
EP - 707
DO - 10.5220/0008985507000707
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic