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.
Downloads of chronos-2 have moved +2.0% over the trailing thirty days. That is a slight downtrend, consistent with normal cooling as newer models compete for the same workloads. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.
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
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
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
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
