arXiv AI June 3, 2026 By Tao Xie, Zexi Tan, Haoyi Xiao, Mengke Li, Yiqun Zhang, Yang Lu, Cuie Yang, Yiu-ming Cheung

AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE

arXiv:2606. 03631v1 Announce Type: cross Abstract: Multivariate time series classification (MTSC) is pivotal in high-stakes domains, such as clinical diagnosis and industrial fault detection, where safe deployment necessitates transparent decision-making.

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benchmarks

arXiv AI

Aug 11

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification

arXiv:2608. 08207v1 Announce Type: cross Abstract: Multivariate Time Series Classification (MTSC) demands models that can effectively capture complex temporal patterns across multiple scales while remaining computationally efficient.

By Pingping Liu, Muyao Wang, Zijian Zhang, Tongshun Zhang, Hao Miao, Guorui Xie, Qingliang Li, Qiuzhan Zhou

benchmarks

arXiv Machine Learning

Jul 21

ProtoTSNet: Interpretable Multivariate Time Series Classification With Prototypical Parts

arXiv:2511. 02152v2 Announce Type: replace Abstract: Time series data is one of the most popular data modalities in critical domains such as industry and medicine.

By Bart{\l}omiej Ma{\l}kus, Szymon Bobek, Grzegorz J. Nalepa

benchmarks safety

arXiv Machine Learning

Jul 31

Information Bottleneck Learning for Faithful Time Series Forecasting Explanations

arXiv:2607. 28124v1 Announce Type: new Abstract: As forecasts increasingly drive decisions in fields such as energy, transportation, and healthcare, understanding the historical data behind these predictions has become as crucial as the predictions themselves.

By Xu Zheng, Wei Cheng, Zhuomin Chen, Mo Sha, Jingchao Ni, Dongsheng Luo

efficiency

arXiv Machine Learning

Sep 10

Improving Multivariate Time Series Classification with Class-Wise Training and Model Aggregation

arXiv:2609.07493v1 Announce Type: new Abstract: In this paper, we propose a class-wise dimension (channel) selection framework for Multivariate Time Series Classification (MTSC). Rather than applying...

By Mouhamadou Mansour Lo, Gildas Morvan, Mathieu Rossi, Fabrice Morganti, David Mercier

safety

arXiv Machine Learning

Jun 19

PaAno+: Multiscale Encoding and Cross-Variable Attention for Time Series Anomaly Detection

arXiv:2606. 20055v1 Announce Type: new Abstract: Time-series anomaly detection has significant practical value for industrial and medical monitoring, as well as other critical domains.

By Youji Zhu, Hongbing Wang, Wenchao Liu, Xiaodong Liu, Xiangguang Xiong

llms rag benchmarks

arXiv AI

Jul 3

LEFT: Learnable Fusion of Tri-view Tokens for Unsupervised Time Series Anomaly Detection

arXiv:2602. 08638v2 Announce Type: replace-cross Abstract: As a fundamental data mining task, unsupervised time series anomaly detection (TSAD) aims to build a model for identifying abnormal timestamps without assuming the availability of annotations.

By Dezheng Wang, Tong Chen, Guansong Pang, Congyan Chen, Shihua Li, Hongzhi Yin

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FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification

ProtoTSNet: Interpretable Multivariate Time Series Classification With Prototypical Parts

Information Bottleneck Learning for Faithful Time Series Forecasting Explanations

Improving Multivariate Time Series Classification with Class-Wise Training and Model Aggregation

PaAno+: Multiscale Encoding and Cross-Variable Attention for Time Series Anomaly Detection

LEFT: Learnable Fusion of Tri-view Tokens for Unsupervised Time Series Anomaly Detection