Go to NeurIPS 2025 Workshop BERT2S homepageKronos: A Foundation Model for the Language of Financial Markets
Keywords: Time Series Foundation Model (TSFM), Financial Time Series, Quantitative Finance, Large-scale Pre-training, Time Series Tokenization, Time Series Forecasting
TL;DR: We introduce Kronos, a foundation model that learns the "language" of financial markets from massive K-line data, setting a new state-of-the-art across a broad spectrum of quantitative tasks.
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 code and models are available at https://anonymous.4open.science/r/Kronos-7B01.
Submission Number: 10
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