Unsupervised Distance Metric Learning for Anomaly Detection Over Multivariate Time Series

2024-03-04 · Hanyang Yuan, Qinglin Cai, Keting Yin

Distance-based time series anomaly detection methods are prevalent due to their relative non-parametric nature and interpretability. However, the commonly used Euclidean distance is sensitive to noise. While existing works have explored dynamic time warping (DTW) for its robustness, they only support supervised tasks over multivariate time series (MTS), leaving a scarcity of unsupervised methods. In this work, we propose FCM-wDTW, an unsupervised distance metric learning method for anomaly detection over MTS, which encodes raw data into latent space and reveals normal dimension relationships through cluster centers. FCM-wDTW introduces locally weighted DTW into fuzzy C-means clustering and learns the optimal latent space efficiently, enabling anomaly identification via data reconstruction. Experiments with 11 different types of benchmarks demonstrate our method's competitive accuracy and efficiency.

📄 PDF Abstract BibTeX arXiv:2403.01895

Code (0)

등록된 구현이 없습니다.

Tasks

Anomaly DetectionDynamic Time WarpingMetric LearningTime SeriesTime Series Anomaly Detection

Methods 이 논문이 사용한 방법론

DTW Dynamic Time Warping (DTW) [1] is one of well-known distance measures between a pairwise of time series. The main idea of DTW is to compute the distance from the matching of…

MTS 설명 없음

Explainable Unsupervised Anomaly Detection with Random Forest

2025-04-22 · Joshua S. Harvey, Joshua Rosaler, Mingshu Li, Dhruv Desai 외

We describe the use of an unsupervised Random Forest for similarity learning and improved unsupervised anomaly detection. By training a Random Forest to discriminate between real data and synthetic data sampled from a un…

Anomaly Detection Feature Importance Unsupervised Anomaly Detection

Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection

2020-10-29 · Imtiaz Ahmed, Travis Galoppo, Xia Hu, Yu Ding

Dimensionality reduction is a crucial first step for many unsupervised learning tasks including anomaly detection and clustering. Autoencoder is a popular mechanism to accomplish dimensionality reduction. In order to mak…

Anomaly Detection Clustering Dimensionality Reduction Unsupervised Anomaly Detection

Bagged Regularized $k$-Distances for Anomaly Detection

2023-12-02 · Yuchao Cai, Yuheng Ma, Hanfang Yang, Hanyuan Hang

We consider the paradigm of unsupervised anomaly detection, which involves the identification of anomalies within a dataset in the absence of labeled examples. Though distance-based methods are top-performing for unsuper…

Anomaly Detection Density Estimation Sensitivity Unsupervised Anomaly Detection

Unsupervised Anomaly Detection in NSL-KDD Using $β$-VAE: A Latent Space and Reconstruction Error Approach

2026-02-23 · Dylan Baptiste, Ramla Saddem, Alexandre Philippot, François Foyer arxiv

As Operational Technology increasingly integrates with Information Technology, the need for Intrusion Detection Systems becomes more important. This paper explores an unsupervised approach to anomaly detection in network…

Unsupervised Anomaly Detection Intrusion Detection

Challenges for Unsupervised Anomaly Detection in Particle Physics

2021-10-13 · Katherine Fraser, Samuel Homiller, Rashmish K. Mishra, Bryan Ostdiek 외

Anomaly detection relies on designing a score to determine whether a particular event is uncharacteristic of a given background distribution. One way to define a score is to use autoencoders, which rely on the ability to…

Anomaly Detection Unsupervised Anomaly Detection