✨Future Is Unevenly Distributed: Forecasting Ability of LLMs Depends on What We're Asking

📝 Summary:
LLM forecasting ability varies significantly across domains and question types. Their predictive performance depends heavily on context, external knowledge, and how questions are asked.

🔹 Publication Date: Published on Nov 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18394
• PDF: https://arxiv.org/pdf/2511.18394

==================================

For more data science resources:
✓ https://xn--r1a.website/DataScienceT

#LLM #Forecasting #AI #MachineLearning #DataScience

✨Kronos: A Foundation Model for the Language of Financial Markets

📝 Summary:
Kronos is a novel foundation model for financial K-line data. It uses a specialized tokenizer and autoregressive pre-training on a vast dataset to significantly outperform existing models in price and volatility forecasting, and synthetic data generation, establishing it as a versatile tool for f...

🔹 Publication Date: Published on Aug 2, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.02739
• PDF: https://arxiv.org/pdf/2508.02739
• Github: https://github.com/shiyu-coder/Kronos

🔹 Models citing this paper:
• https://huggingface.co/NeoQuasar/Kronos-base
• https://huggingface.co/NeoQuasar/Kronos-Tokenizer-base
• https://huggingface.co/NeoQuasar/Kronos-mini

✨ Spaces citing this paper:
• https://huggingface.co/spaces/ByronWang2005/Kronos-CS2-Skins-Forecast-Demo
• https://huggingface.co/spaces/yangyang158/kronos
• https://huggingface.co/spaces/heyunfei/crypt

==================================

For more data science resources:
✓ https://xn--r1a.website/DataScienceT

#FoundationModel #FinancialAI #DeepLearning #QuantitativeFinance #Forecasting