Cover Image

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Boggia, Laura, de Lima, Rafael Teixeira, Malaescu, Bogdan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Synthetic Data Generation with Lorenzetti for Time Series Anomaly Detection in High-Energy Physics Calorimeters
by: Boggia, Laura, et al.
Published: (2025)

Causal Discovery in Multivariate Time Series through Mutual Information Featurization
by: Paldino, Gian Marco, et al.
Published: (2025)

Robust Spectral Fuzzy Clustering of Multivariate Time Series with Applications to Electroencephalogram
by: Ma, Ziling, et al.
Published: (2025)

Forecasting Multivariate Time Series under Predictive Heterogeneity: A Validation-Driven Clustering Framework
by: Ma, Ziling, et al.
Published: (2026)

Causal and Local Correlations Based Network for Multivariate Time Series Classification
by: Du, Mingsen, et al.
Published: (2024)

Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics
by: de Souza, Douglas Baptista, et al.
Published: (2024)

Automatic Change-Point Detection in Time Series via Deep Learning
by: Li, Jie, et al.
Published: (2022)

Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains
by: Jacob, Vincent, et al.
Published: (2025)

Localized Sparse Principal Component Analysis of Multivariate Time Series in Frequency Domain
by: Namdari, Jamshid, et al.
Published: (2024)

Autoregressive with Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series
by: Okuno, Akifumi, et al.
Published: (2023)

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series
by: Soni, Juhi, et al.
Published: (2025)

An Open-Access Benchmark of Statistical and Machine-Learning Anomaly Detection Methods for Battery Applications
by: Pang, Mei-Chin, et al.
Published: (2025)

Robust Spatiotemporally Contiguous Anomaly Detection Using Tensor Decomposition
by: Mondal, Rachita, et al.
Published: (2025)

Sliding-Window Signatures for Time Series: Application to Electricity Demand Forecasting
by: Drobac, Nina, et al.
Published: (2025)

Time Series Gaussian Chain Graph Models
by: Fang, Qin, et al.
Published: (2026)

Geometric-Based Pruning Rules For Change Point Detection in Multiple Independent Time Series
by: Pishchagina, Liudmila, et al.
Published: (2023)

Contrast to Detect: Dynamic Graph Contrastive Regularization for Unsupervised Anomaly Detection in Multivariate Time Series
by: Pei, Yunhua, et al.
Published: (2026)

Interventional Time Series Priors for Causal Foundation Models
by: Thumm, Dennis, et al.
Published: (2026)

An Online Bootstrap for Time Series
by: Palm, Nicolai, et al.
Published: (2023)

Unsupervised Distance Metric Learning for Anomaly Detection Over Multivariate Time Series
by: Yuan, Hanyang, et al.
Published: (2024)

SPINEX-TimeSeries: Similarity-based Predictions with Explainable Neighbors Exploration for Time Series and Forecasting Problems
by: Naser, Ahmed Z, et al.
Published: (2024)

Variable Selection for Comparing High-dimensional Time-Series Data
by: Mitsuzawa, Kensuke, et al.
Published: (2024)

Spectral Differential Network Analysis for High-Dimensional Time Series
by: Hellstern, Michael, et al.
Published: (2024)

ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting
by: Wang, Tingting, et al.
Published: (2026)

Benchmarking Tabular Foundation Models for Conditional Density Estimation in Regression
by: Izbicki, Rafael, et al.
Published: (2026)

SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities
by: Lalou, Yanis, et al.
Published: (2024)

Calibrated Multivariate Regression with Localized PIT Mappings
by: Kock, Lucas, et al.
Published: (2024)

Multivariate and Online Transfer Learning with Uncertainty Quantification
by: Hickey, Jimmy, et al.
Published: (2024)

Between Resolution Collapse and Variance Inflation: Weighted Conformal Anomaly Detection in Low-Data Regimes
by: Hennhöfer, Oliver, et al.
Published: (2026)

LLM-Powered CPI Prediction Inference with Online Text Time Series
by: Fan, Yingying, et al.
Published: (2025)

MMM: Clustering Multivariate Longitudinal Mixed-type Data
by: Amato, Francesco, et al.
Published: (2025)

MTAD: Tools and Benchmarks for Multivariate Time Series Anomaly Detection
by: Liu, Jinyang, et al.
Published: (2024)

Structural Classification of Locally Stationary Time Series Based on Second-order Characteristics
by: Qian, Chen, et al.
Published: (2025)

Classification of High-dimensional Time Series in Spectral Domain using Explainable Features
by: Roy, Sarbojit, et al.
Published: (2024)

Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity
by: Kamarthi, Harshavardhan, et al.
Published: (2024)

Improving Forecasts for Heterogeneous Time Series by "Averaging", with Application to Food Demand Forecast
by: Neubauer, Lukas, et al.
Published: (2023)

Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning
by: Wu, Xiangkun, et al.
Published: (2026)

Fitting Sparse Markov Models to Categorical Time Series Using Convex Clustering
by: Majumder, Tuhin, et al.
Published: (2022)

DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Series
by: Wu, Dongze, et al.
Published: (2025)

Fast Training of Mixture-of-Experts for Time Series Forecasting via Expert Loss Integration
by: Mahtout, Btissame El, et al.
Published: (2026)