@inproceedings{pei-etal-2025-leaf,
    title = "{LEAF}: Large Language Diffusion Model for Time Series Forecasting",
    author = "Pei, Yuhang  and
      Ren, Tao  and
      Wang, Yifan  and
      Sun, Zhipeng  and
      Ju, Wei  and
      Chen, Chong  and
      Hua, Xian-Sheng  and
      Luo, Xiao",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.58/",
    doi = "10.18653/v1/2025.findings-emnlp.58",
    pages = "1076--1091",
    ISBN = "979-8-89176-335-7",
    abstract = "This paper studies the problem of time series forecasting, which aims to generate future predictions given historical trajectories. Recent researchers have applied large language models (LLMs) into time series forecasting, which usually align the time series space with textual space and output future predictions with strong autoregressive reasoning abilities. Despite their remarkable progress, these approaches usually lack an understanding of holistic temporal patterns with potential error accumulation. Towards this end, this paper proposes a simple yet effective framework that marries $\underline{\text{L}}$arg$\underline{\text{e}}$ Langu$\underline{\text{a}}$ge Diffusion Model with time series $\underline{\text{f}}$orecasting (LEAF). The core of our framework is to generate future predictions with a diffusion model from a holistic view. In particular, we first introduce a tokenization module to convert time series into tokens and then adopt the language diffusion models to capture the temporal dependencies. In this way, we can transform masked time series into all the predictions with the remasking strategy. Extensive experiments on various benchmark datasets validate the effectiveness of the proposed LEAF in comparison to various baselines."
}