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News/model/chronos-2

chronos-2 news

6 articles mentioning chronos-2

arxivMay 13

Investigating simple target-covariate relationships for Chronos-2 and TabPFN-TS

arXiv:2605.12200v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) have recently achieved state-of-the-art performance, often outperforming supervised models in zero-shot settings. Recent TSFM architectures, such as Chronos-2 and TabPFN-TS, aim to integrate covariates. In this pap

arxivJun 18

ChronoSurv: A Clinical Pathway-Guided Graph Framework for Multimodal Survival Analysis

arXiv:2606.19140v1 Announce Type: new Abstract: Accurate survival prediction is essential for personalized treatment planning in head and neck cancer, yet remains challenging due to the heterogeneous and high-dimensional nature of multimodal clinical data. While deep survival models have improved pr

arxivJun 2

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection

arXiv:2606.01300v1 Announce Type: cross Abstract: Time series anomaly detection is a crucial task in various domains, including finance, healthcare, and industry. However, existing methods often struggle to generalize across different datasets, especially when anomalies are subtle or context-depende

arxivMay 25

CHRONOS: Temporally-Aware Multi-Agent Coordination for Evolving Data Marketplaces

arXiv:2605.23887v1 Announce Type: cross Abstract: Temporal knowledge-graph data marketplaces face three coupled failures in static designs: stale hybrid index shortcuts reduce recall as edges evolve, stationary Shapley pricing misattributes value after distribution shifts, and uncoordinated agents o

arxivMay 8

Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction

arXiv:2605.06361v1 Announce Type: new Abstract: This paper presents a preliminary analysis of the ability of Chronos foundation model to process and internally represent frequency domain information. Foundation models that process time-series data offer practitioners a unified architecture capable o

arxivMay 8

ChronoSpike: An Adaptive Spiking Graph Neural Network for Dynamic Graphs

arXiv:2602.01124v3 Announce Type: replace Abstract: Dynamic graph representation learning requires capturing both structural relations and temporal evolution, yet existing approaches face a core trade-off: attention-based methods offer expressiveness at $O(T^2)$ complexity, while recurrent architect

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