arXiv AI June 2, 2026 By Haoji Hu, Huaqing Mao, Yijun Lin, Xiaowei Jia, Jinwei Zhou, Minoh Jeong, Yao-Yi Chiang

Estimating Mutual Information between Time Series and Temporal Event Sequences Across Diverse Analysis Tasks

arXiv:2606. 01602v1 Announce Type: cross Abstract: Pairwise dependence measures such as correlation and causality are fundamental to temporal data mining, yet there is still no principled and robust way to quantify dependence between heterogeneous data types, especially between continuous time series and discrete temporal event sequences.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 3

Your Autoregressive Model Already Reveals the Causal Graph

arXiv:2602. 01135v3 Announce Type: replace Abstract: Autoregressive models trained via next-token prediction implicitly learn the conditional independence structure of their data-generating process.

By Hugo Math, Rainer Lienhart

arXiv Machine Learning

Jun 18

TimeLAVA: Learning-Agnostic Data Valuation for Time Series

arXiv:2606. 18729v1 Announce Type: cross Abstract: Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning.

By Wenqin Liu, Weizhi Quan, Aoqi Zuo, Erdun Gao, Vu Nguyen, Dino Sejdinovic, Howard Bondell, Mingming Gong

efficiency

arXiv AI

Jun 3

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.

By Tao Xie, Zexi Tan, Haoyi Xiao, Mengke Li, Yiqun Zhang, Yang Lu, Cuie Yang, Yiu-ming Cheung

benchmarks

arXiv AI

Jul 24

Beyond Sufficiency: Time Series Explanation with Counterfactual Necessity

arXiv:2607. 21573v1 Announce Type: cross Abstract: Faithful explanations of time-series classifiers should identify subsequences that are not only sufficient to preserve a black-box model's prediction, but also necessary for maintaining it.

By Hongnan Ma, Yiwei Shi, Mengyue Yang, Weiru Liu

benchmarks

arXiv Machine Learning

Jun 30

TimeLAVA: Learning-Agnostic Valuation for Time Series Data

arXiv:2606. 18729v3 Announce Type: replace-cross Abstract: Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning.

By Wenqin Liu, Weizhi Quan, Aoqi Zuo, Erdun Gao, Vu Nguyen, Dino Sejdinovic, Howard Bondell, Mingming Gong

efficiency

arXiv AI

Jun 29

Global Explanations for Multivariate Time Series Forecasting Models via $K$-Order Markov Approximations

arXiv:2606. 27599v1 Announce Type: cross Abstract: While many explainable AI (XAI) methods have been proposed, most are not designed for time-series forecasting models and often rely on the implicit assumption that timestamp features are independent.

By Amadeo Tunyi

Related stories

Your Autoregressive Model Already Reveals the Causal Graph

TimeLAVA: Learning-Agnostic Data Valuation for Time Series

AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE

Beyond Sufficiency: Time Series Explanation with Counterfactual Necessity

TimeLAVA: Learning-Agnostic Valuation for Time Series Data

Global Explanations for Multivariate Time Series Forecasting Models via $K$-Order Markov Approximations