Author ORCID Identifier
https://orcid.org/0000-0001-6462-3248
Document Type
Article
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
Computer Sciences
Abstract
This paper examines how data normalisation and clustering interact in the definition of sub-domains within multi-source transfer learning systems for time series anomaly detection. The paper introduces a distinction between (i) clustering as a primary/direct method for anomaly detection, and (ii) clustering as a method for identifying sub-domains within the source or target datasets. Reporting the results of three sets of experiments, we find that normalisation after feature extraction and before clustering results in the best performance for anomaly detection. Interestingly, we find that in the multi-source transfer learning scenario clustering on the target dataset and identifying subdomains in the target data can result in improved model performance, as compared to identifying sub-domains through defining clusters using the multi-source dataset.
DOI
https://doi.org/10.1016/j.knosys.2022.109894
Recommended Citation
Matthew Nicholson, Rahul Agrahari, Clare Conran, Haythem Assem, John D. Kelleher, The interaction of normalisation and clustering in sub-domain definition for multi-source transfer learning based time series anomaly detection, Knowledge-Based Systems, Volume 257, 2022, 109894, ISSN 0950-7051, DOI: 10.1016/j.knosys.2022.109894.
Funder
SFI ADAPT Centre
Publication Details
Knowledge-Based Systems, Volume 257, 5 December 2022, 109894
Open access
https://www.sciencedirect.com/science/article/pii/S095070512200987X#d1e1490