loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Dieter De Paepe ; Olivier Janssens and Sofie Van Hoecke

Affiliation: Ghent University – imec – IDLab, Department of Electronics and Information Systems, Ghent and Belgium

Keyword(s): Matrix Profile, Noise, Time Series, Anomaly Detection, Time Series Segmentation.

Related Ontology Subjects/Areas/Topics: Applications ; Data Engineering ; Information Retrieval ; Ontologies and the Semantic Web ; Pattern Recognition ; Software Engineering

Abstract: As companies are increasingly measuring their products and services, the amount of time series data is rising and techniques to extract usable information are needed. One recently developed data mining technique for time series is the Matrix Profile. It consists of the smallest z-normalized Euclidean distance of each subsequence of a time series to all other subsequences of another series. It has been used for motif and discord discovery, for segmentation and as building block for other techniques. One side effect of the z-normalization used is that small fluctuations on flat signals are upscaled. This can lead to high and unintuitive distances for very similar subsequences from noisy data. We determined an analytic method to estimate and remove the effects of this noise, adding only a single, intuitive parameter to the calculation of the Matrix Profile. This paper explains our method and demonstrates it by performing discord discovery on the Numenta Anomaly Benchmark and by segmenti ng the PAMAP2 activity dataset. We find that our technique results in a more intuitive Matrix Profile and provides improved results in both usecases for series containing many flat, noisy subsequences. Since our technique is an extension of the Matrix Profile, it can be applied to any of the various tasks that could be solved by it, improving results where data contains flat and noisy sequences. (More)

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 3.235.145.108

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:
De Paepe, D.; Janssens, O. and Van Hoecke, S. (2019). Eliminating Noise in the Matrix Profile. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-351-3; ISSN 2184-4313, SciTePress, pages 83-93. DOI: 10.5220/0007314100830093

@conference{icpram19,
author={Dieter {De Paepe}. and Olivier Janssens. and Sofie {Van Hoecke}.},
title={Eliminating Noise in the Matrix Profile},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2019},
pages={83-93},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007314100830093},
isbn={978-989-758-351-3},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Eliminating Noise in the Matrix Profile
SN - 978-989-758-351-3
IS - 2184-4313
AU - De Paepe, D.
AU - Janssens, O.
AU - Van Hoecke, S.
PY - 2019
SP - 83
EP - 93
DO - 10.5220/0007314100830093
PB - SciTePress