Model Detail
chronos-2
—chronos-2 is an AI model with 60M parameters released by amazon. The model is registered under the time-series-forecasting pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.
chronos-2 ships with 60M parameters. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
chronos-2 is best fit for workloads that match the time-series-forecasting pipeline tag. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
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
The Chronos Vulnerability: A Taxonomy of Temporal Persistence and Memory-Based Deception in Agentic AI
arXiv:2607.19433v1 Announce Type: new Abstract: The transition from stateless generative models in artificial intelligence to stateful, autonomous agents represents an architectural evolution that, while providing the capabilities of long-term planning and the automation of enterprise workflows, als
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
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
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
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
