arXiv AI

Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate

arXiv:2606. 02670v1 Announce Type: cross Abstract: Many recent multivariate time series anomaly detection (MT-SAD) models incorporate cross-channel modeling, under the implicit assumption that the structure of anomalies may be spread across multiple channels.

arXiv AI

4d ago

Channel-Dependent State Space Model for Multivariate Time Series Forecasting

The paper introduces Chameleon, a channel‑dependent state space model for multivariate time series forecasting that allows data‑dependent, fine‑grained interactions across variables while maintaining linear scaling with the number of variables. By integrating selective state space models with a Kalman filter and adapting GatedDeltaNet as the backbone, Chameleon improves generalization and achieves lower MSE and MAE on strongly dependent ODE and PEMS datasets compared to both channel‑independent and prior channel‑dependent methods. Across 28 benchmark settings, it outperforms baselines in the majority of cases and demonstrates competitive training‑time and memory efficiency on Traffic and ETT datasets.

By Yu-Cheng Wu, Fan-Keng Sun, Li-Chun Lu, Duane S. Boning

benchmarks

arXiv AI

Jun 4

An Empirical Audit of Input Encoders for Multi-Channel Signal Transformers

arXiv:2606. 04752v1 Announce Type: cross Abstract: Transformers consuming multi-channel scalar signals must embed $C$ simultaneous values into one $d_{\text{model}}$-dimensional vector per time step.

By Ossi Lehtinen

llms benchmarks

arXiv AI

Jun 9

PAI: Preserving Amplitude Information in Representation-Based Time-Series Anomaly Detection

arXiv:2606. 08935v1 Announce Type: cross Abstract: Representation-based time-series anomaly detection algorithms significantly outperform other methods on diverse anomaly detection tasks.

By Kang Zhang, Wei Jian Lau, Shoushou Ren, Dong Lin, Joon Son Chung, Chuanhao Sun

rag benchmarks

arXiv AI

Sep 15

Diagnosing Temporal Misalignment in Multichannel Time-Series Classification with Minimum Description Length

arXiv:2609.14595v1 Announce Type: cross Abstract: Multichannel time-series classification commonly assumes synchronized sensor streams, although latency, clock drift, and preprocessing can introduce...

By Sebastian Buschj\"ager, Michael Frichert, Daniel Kuhe, Jian-Jia Chen

benchmarks safety

arXiv AI

Aug 6

Just Repair: A Minimal Denoising Network for Time Series Anomaly Detection

arXiv:2604. 17388v3 Announce Type: replace-cross Abstract: Time series anomaly detectors have grown steadily more complex, incorporating attention mechanisms, adversarial training, and stochastic latent variables.

By Kadir-Kaan \"Ozer, Ren\'e Ebeling, Markus Enzweiler

benchmarks safety

arXiv Machine Learning

Jul 20

Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source

arXiv:2605. 31259v2 Announce Type: replace Abstract: Unscheduled trips of high-power pulsed converters are a leading source of downtime at large accelerator facilities.

By Alberto D. Cencillo, Leonardo Concepci\'on, Juli\'an Luengo, Isaac Triguero

safety

arXiv Machine Learning

2d ago

LEARN-TS: LLM-Enhanced Alignment and Reconstruction with Normality Guidance for Multivariate Time-Series Anomaly Detection

arXiv:2609.38789v1 Announce Type: new Abstract: Reconstruction errors in multivariate time-series anomaly detection may not reliably distinguish abnormal behavior from benign deviations. Language-der...

By Jahyeob Koo, Kio Yun, Byoungmo Koo, Jun-Geol Baek

llms multimodal benchmarks safety

arXiv Machine Learning

Jun 10

Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection

arXiv:2606. 09874v1 Announce Type: new Abstract: Reconstruction-based methods are widely used for time series anomaly detection, where models are trained to reconstruct subsequences, and anomalies are identified through reconstruction errors.

By Guillaume Coulaud (UM, IROKO), Reza Akbarinia (IROKO), Florent Masseglia (IROKO)

llms benchmarks

arXiv Machine Learning

Jul 8

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates

arXiv:2607. 05452v1 Announce Type: new Abstract: Time series forecasters that use exogenous covariates are fragile in deployment: when those covariates are noised, temporally misaligned, or missing, strong exogenous-fusion and exogenous-adapted models can degrade far above the endogenous-only floor.

By Hao Hu, Xue-shan Ai

benchmarks

arXiv AI

Aug 5

PRISM: Powerful Time Series to Image (TS2I) Representations for Multivariate Anomaly Detection

arXiv:2608. 03926v1 Announce Type: cross Abstract: Time series anomaly detection (TSAD) underpins applications in predictive maintenance, finance, and cloud computing, however performance remains sensitive to representation choices, especially in multivariate settings.

By Mateusz Smendowski, Kamil Faber, Piotr Nawrocki, Nathalie Japkowicz, Roberto Corizzo

fine-tuning

arXiv Machine Learning

Jun 5

Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series

arXiv:2506. 00188v2 Announce Type: replace Abstract: Early and accurate detection of anomalies in time-series data is critical due to the substantial risks associated with false or missed detections.

By Md Mahmuddun Nabi Murad, Yasin Yilmaz

rag benchmarks

arXiv Machine Learning

Aug 13

JAPE: Joint Anomaly Prediction and Intrinsic Explanation in Multivariate Time Series

arXiv:2608. 11801v1 Announce Type: new Abstract: Multivariate time-series anomaly prediction aims to identify whether and when anomalies will occur over a future horizon from historical observations.

By Yian Wei, Yuanyuan Yao, Lu Chen, Xiangmin Zhou, Tianyi Li

benchmarks

Related stories