The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/15ba3f7a-cd49-4c24-86e5-084e2e9276df

Ghorbani, Ramin; Reinders, Marcel; Tax, David (2024): Code underlying the publication: "RESTAD: Reconstruction and Similarity Transformer for time series Anomaly Detection". Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/15ba3f7a-cd49-4c24-86e5-084e2e9276df.v1

Software

• Artificial Intelligence and Image Processing
• Information and Computing Sciences

Anomaly Detection, Radial Basis Function (RBF) kernel, Time Series, Transformer

MIT

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Ramin Ghorbani , Marcel Reinders , David Tax

This repository contains the official implementation of RESTAD (REconstruction and Similarity-based Transformer for time series Anomaly Detection), a novel framework that integrates reconstruction error with Radial Basis Function (RBF) similarity scores to enhance sensitivity to subtle anomalies. RESTAD leverages a Transformer architecture with an embedded RBF layer to synergistically detect anomalies in time series data, outperforming existing baselines on multiple benchmark datasets.

• 2024-12-20 first online, published, posted

4TU.ResearchData

Script/.py Configuration/.json Documentation/.md Dependency/.txt License/LICENSE Version Control/.gitignore, .gitattributes

RESTAD: Reconstruction and Similarity Transformer for time series Anomaly Detection

• https://doi.org/10.1109/MLSP58920.2024.10734755

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Pattern Recognition & Bioinformatics Group