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

arXiv: https://arxiv.org/abs/2508.02739 · HTML: https://mlsanity.com/paper/2508.02739/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 52 references that are in the map.
- First posted on arXiv 2 Aug 2025.

## Builds on (most cited first, 26 of 26)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Root Mean Square Layer Normalization](https://mlsanity.com/paper/1910.07467/) (2019)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [On Layer Normalization in the Transformer Architecture](https://mlsanity.com/paper/2002.04745/) (2020)
- [Chronos: Learning the Language of Time Series](https://mlsanity.com/paper/2403.07815/) (2024)
- [Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation](https://mlsanity.com/paper/2310.05737/) (2023)
- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts](https://mlsanity.com/paper/2409.16040/) (2024)
- [TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables](https://mlsanity.com/paper/2402.19072/) (2024)
- [Deep Time Series Models: A Comprehensive Survey and Benchmark](https://mlsanity.com/paper/2407.13278/) (2024)
- [Diffusion-TS: Interpretable Diffusion for General Time Series Generation](https://mlsanity.com/paper/2403.01742/) (2024)
- [Sundial: A Family of Highly Capable Time Series Foundation Models](https://mlsanity.com/paper/2502.00816/) (2025)
- [TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation](https://mlsanity.com/paper/2111.08095/) (2021)
- [Image and Video Tokenization with Binary Spherical Quantization](https://mlsanity.com/paper/2406.07548/) (2024)
- [Qlib: An AI-oriented Quantitative Investment Platform](https://mlsanity.com/paper/2009.11189/) (2020)
- [Addressing Representation Collapse in Vector Quantized Models with One Linear Layer](https://mlsanity.com/paper/2411.02038/) (2024)
- [Towards Neural Scaling Laws for Time Series Foundation Models](https://mlsanity.com/paper/2410.12360/) (2024)
- [Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation](https://mlsanity.com/paper/2503.16430/) (2025)
- [MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model](https://mlsanity.com/paper/2409.07486/) (2024)
- [The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models](https://mlsanity.com/paper/2005.10111/) (2020)
- [LENS: Large Pre-trained Transformer for Exploring Financial Time Series Regularities](https://mlsanity.com/paper/2408.10111/) (2024)

## Built on by (most cited first, 26 of 26)

- [Evaluating LLMs in Finance Requires Explicit Bias Consideration](https://mlsanity.com/paper/2602.14233/) (2026)
- [Unlocking Data Value in Finance: A Study on Distillation and Difficulty-Aware Training](https://mlsanity.com/paper/2603.07223/) (2026)
- [Bridging the Last Mile of Time Series Forecasting with LLM Agents](https://mlsanity.com/paper/2606.02497/) (2026)
- [Are Foundation Models Useful for Bankruptcy Prediction?](https://mlsanity.com/paper/2511.16375/) (2025)
- [MEME: Modeling the Evolutionary Modes of Financial Markets](https://mlsanity.com/paper/2602.11918/) (2026)
- [WindFM: An Open-Source Foundation Model for Zero-Shot Wind Power Forecasting](https://mlsanity.com/paper/2509.06311/) (2025)
- [Hindsight Preference Optimization for Financial Time Series Advisory](https://mlsanity.com/paper/2604.23988/) (2026)
- [Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs](https://mlsanity.com/paper/2605.21975/) (2026)
- [Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection](https://mlsanity.com/paper/2605.27470/) (2026)
- [H3M-SSMoEs: Hypergraph-based Multimodal Learning with LLM Reasoning and Style-Structured Mixture of Experts](https://mlsanity.com/paper/2510.25091/) (2025)
- [GS-FUSE: Granger-Supervised Gated Fusion and Multi-Granularity Alignment for Event-Driven Financial Forecasting](https://mlsanity.com/paper/2605.28520/) (2026)
- [Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling](https://mlsanity.com/paper/2605.30376/) (2026)
- [Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting](https://mlsanity.com/paper/2606.08630/) (2026)
- [FMplex: Model Virtualization for Serving Extensible Foundation Models](https://mlsanity.com/paper/2606.09643/) (2026)
- [Time Series as Language: A Universal Tokenizer for General-Purpose Time Series Foundation Models](https://mlsanity.com/paper/2606.09861/) (2026)
- [RAVEN: A Regime-Aware Variable-context Expert Network for Financial Time Series Forecasting](https://mlsanity.com/paper/2606.24062/) (2026)
- [VAIOM: Continuous-Input, Discrete-Output Decoder-Only Financial Sequence Modeling](https://mlsanity.com/paper/2607.13929/) (2026)
- [Can Large Language Models Execute Parent Orders?](https://mlsanity.com/paper/2607.28410/) (2026)
- [Financial Numerical Prediction and Allocation as Token Generation](https://mlsanity.com/paper/2608.09880/) (2026)
- [M3: A State-Event Generative Foundation Model for Market Microstructure Dynamics](https://mlsanity.com/paper/2608.19227/) (2026)
- [LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs](https://mlsanity.com/paper/2609.33470/) (2026)
- [KiT: A Foundation Model for Financial Time-Series Forecasting using DiffusionTransformers](https://mlsanity.com/paper/2609.34507/) (2026)
- [HALO: Enhancing Time Series Generation via Hyperspherical Latents and Masked AutoregRessive Modeling](https://mlsanity.com/paper/2609.34511/) (2026)
- [Can Language Models Learn to Forecast Stock Prices](https://mlsanity.com/paper/2609.36914/) (2026)
- [Volatility-Clustering Adaptation for Financial Time Series](https://mlsanity.com/paper/2609.37715/) (2026)
- [DIVINE: Simple Cross-Market Stock Pretraining via Diverse Indicator Reconstruction](https://mlsanity.com/paper/2610.02866/) (2026)

## Abstract (arXiv)

The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs). However, their application to financial candlestick (K-line) data remains limited, often underperforming non-pre-trained architectures. Moreover, existing TSFMs often overlook crucial downstream tasks such as volatility prediction and synthetic data generation. To address these limitations, we propose Kronos, a unified, scalable pre-training framework tailored to financial K-line modeling. Kronos introduces a specialized tokenizer that discretizes continuous market information into token sequences, preserving both price dynamics and trade activity patterns. We pre-train Kronos using an autoregressive objective on a massive, multi-market corpus of over 12 billion K-line records from 45 global exchanges, enabling it to learn nuanced temporal and cross-asset representations. Kronos excels in a zero-shot setting across a diverse set of financial tasks. On benchmark datasets, Kronos boosts price series forecasting RankIC by 93% over the leading TSFM and 87% over the best non-pre-trained baseline. It also achieves a 9% lower MAE in volatility forecasting and a 22% improvement in generative fidelity for synthetic K-line sequences. These results establish Kronos as a robust, versatile foundation model for end-to-end financial time series analysis. Our pre-trained model is publicly available at https://github.com/shiyu-coder/Kronos.