Manifold-regularised Large-Margin ℓ_p-SVDD for Multidimensional Time Series Anomaly Detection

2025/07/31 by Arashloo, Shervin Rahimzadeh
#FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2507.23449

Abstract

We generalise the recently introduced large-margin ℓ_p-SVDD approach to exploit the geometry of data distribution via manifold regularising for time series anomaly detection. Specifically, we formulate a manifold-regularised variant of the ℓ_p-SVDD method to encourage label smoothness on the underlying manifold to capture structural information for improved detection performance. Drawing on an existing Representer theorem, we then provide an effective optimisation technique for the proposed method. We theoretically study the proposed approach using Rademacher complexities to analyse its generalisation performance and also provide an experimental assessment of the proposed method across various data sets to compare its performance against other methods.

Citations

• TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
• TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data
• Accelerated Graph Learning from Smooth Signals
• Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
• TS2Vec: Towards Universal Representation of Time Series
• Multivariate Time-series Anomaly Detection via Graph Attention Network
• A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an\n LSTM-based Variational Autoencoder
• Kernels for sequentially ordered data

Related

• Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach
• Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach
• Warping Resilient Scalable Anomaly Detection in Time Series
• Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series
• Deep Convolutional Large-Margin ℓ_p-SVDD for Visual Anomaly Detection