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Next-Gen Predictive Models Revolutionize Forecasting

Groundbreaking advancements are emerging in predictive modeling, leveraging innovative foundation models to transform analytical capabilities. New research unveils Google Research's TimesFM-ICF, a novel model demonstrating few-shot learning directly from in-context examples at inference time, which bypasses the need for complex supervised fine-tuning and significantly boosts prediction accuracy. Complementing this, other pioneering models like Chronos are adapting LLM-inspired architectures to address intricate scientific challenges, from chaotic systems to complex spatiotemporal dynamics. These developments underscore a critical focus on models that not only satisfy physical constraints and quantify uncertainty but also deliver robust probabilistic predictions, making advanced, trustworthy, and accessible data-driven decision-making a reality across diverse applications.

calendar_today 2025-09-23 attribution research.google/blog/

Time series foundation models can be few-shot learners

Revolutionizing time-series forecasting, Google Research unveils TimesFM-ICF, a novel foundation model enabling few-shot learning directly from in-context examples at inference time. This breakthrough eliminates the complexities of supervised fine-tuning, democratizing access to high-end forecasting. By employing continued pre-training with unique separator tokens, TimesFM-ICF learns to adapt efficiently from relevant historical data. It achieves a 6.8% accuracy improvement over its base model and matches the performance of fine-tuned models, making powerful, adaptable forecasting more accessible and accelerating data-driven business decisions.
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calendar_today 2025-09-26 attribution www.amazon.science/blog

Science in the age of foundation models

Foundation models (FMs), inspired by LLMs, are poised to revolutionize scientific domains like CFD and weather forecasting, but require specific adaptations. This post highlights the critical need for FMs in science to satisfy physical constraints, quantify uncertainty, and overcome data scarcity. Introducing Chronos, a novel time series foundation model, it demonstrates how LLM-inspired architectures can effectively predict chaotic systems and complex spatiotemporal dynamics. The discussion emphasizes that rigorous scientific applications demand models capable of adhering to physical laws and providing robust probabilistic predictions for trustworthy decision-making.
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