Kronos: A Foundation Model for the Language of Financial Markets

Paper: arXiv 2508.02739

Authors: Yu Shi, Zongliang Fu, Shuo Chen, Bohan Zhao, Wei Xu, Changshui Zhang, Jian Li

Abstract

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.

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 9.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced neural network architectures with specialized quantization and autoregressive modeling (high math), while backing it up with massive-scale pre-training on 12 billion records across 45 exchanges and extensive benchmarking across multiple downstream tasks including forecasting, volatility, and generation (high empirical rigor).

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Develop a robust Time Series Foundation Model<br>for financial K-line data?"] --> B["Methodology:<br>Specialized Tokenizer + Autoregressive Pre-training"]
  B --> C["Input Data:<br>12B K-line records from 45 global exchanges"]
  C --> D["Process:<br>Pre-train Kronos Model"]
  D --> E["Key Findings (Zero-Shot):"]
  E --> F["+93% RankIC<br>Price Forecasting"]
  E --> G["-9% MAE<br>Volatility Prediction"]
  E --> H["+22% Fidelity<br>Synthetic Data Generation"]

Cited References (61)

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3. Learning transferable visual models from natural language supervision (2021)
4. Segment anything (2023)
5. TimeGPT-1 (2023) — link
6. Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts (2025)
7. Japanese candlestick charting techniques: a contemporary guide to the ancient investment techniques of the Far East (2001)
8. itransformer: Inverted transformers are effective for time series forecasting (2023) — link

3 of 61 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.