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TokenCast is a novel framework that leverages Large Language Models (LLMs) for context-aware time series forecasting, by transforming continuous time series into discrete symbolic tokens. It enables a unified generative modeling over both temporal and textual modalities.

📝 “From Values to Tokens: An LLM-Driven Framework for Context-aware Time Series Forecasting via Symbolic Discretization”
Under review | 📄 Paper

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🔍 Overview

Traditional forecasting models struggle to effectively integrate heterogeneous contextual data like clinical notes, policy documents, or logs. TokenCast introduces a new paradigm:

• Converts time series into discrete temporal tokens via dynamic vector quantization.
• Embeds both temporal and textual tokens into a shared semantic space using a frozen pre-trained LLM.
• Performs prompt-based generative forecasting using autoregressive language modeling.

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✨ Key Features

• ✅ Discretized Temporal Modeling: Learnable, reversible tokenizer for symbolic time series.
• 🔗 Cross-Modality Alignment: Unified vocabulary space for both time and text tokens.
• 📈 Prompt-driven Generation: Forecasting with LLM via token-level instruction generation.
• 📊 Multi-domain Evaluation: Benchmarked across economic, health, web, stock, and environmental domains.
• 🌡️ Uncertainty Quantification: Predictive intervals with temperature-controlled generation.

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🚀 Getting Started

1. Clone the repo

git clone https://github.com/Xiaoyu-Tao/TokenCast.git
cd TokenCast

2. Environment Setup

conda create -n tokencast python=3.10
conda activate tokencast
pip install -r requirements.txt

3. Prepare Data

TokenCast supports multiple publicly available datasets:

• Economic (FRED-MD)
• Health (Covid-19 mobility)
• Web (Wikipedia pageviews)
• Stock-NY & Stock-NA (NYSE/NASDAQ)
• Nature (Environmental sensor data)

First, the training and evaluation datasets used in our experiments can be found in Google Drive. Then, create a directory named datasets and download the necessary datasets into it.

mkdir datasets

4. Train the Time Series Tokenizer

sh Tokenizer/scripts/Czelan.sh 

5. Align Embeddings with LLM

sh scripts/pretrain/Czelan.sh  

6. Fine-tune Forecasting Model

sh scripts/finetune/Czelan.sh 

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📊 Benchmark Results

Full Results:

Ablation Results:

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📚 Citation

If you find this project useful, please consider citing our paper:

@inproceedings{tao2026tokencast,
  title={From Values to Tokens: An LLM-Driven Framework for Context-aware Time Series Forecasting via Symbolic Discretization},
  author={Tao, Xiaoyu and Zhang, Shilong and Cheng, Mingyue and Wang, Daoyu and Pan, Tingyue and Pan, Bokai and Zhang, Changqing and Wang, Shijin},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2026}
}

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🤝 Acknowledgements

This project is developed by researchers from:

• 🧠 University of Science and Technology of China (USTC)
• 🧮 Tianjin University
• 🗣️ iFLYTEK Research

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📬 Contact

For questions or collaborations, please contact:

• 🧑‍🏫 Mingyue Cheng (mycheng@ustc.edu.cn)
• 🤖 Xiaoyu Tao (txytiny@mail.ustc.edu.cn)

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📌 License

This project is released under the MIT License.