CoGenCast：用于时间序列预测的耦合自回归流生成框架
CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting
AI总结：
针对时间序列预测需兼顾语义理解与随机建模的问题，提出CoGenCast框架，将预训练大语言模型与流匹配机制结合，通过修改注意力拓扑重配置模型，集成流匹配机制捕捉动态，实现多模态预测和跨域统一训练，实验显示性能具竞争力。
摘要：
时间序列预测可视为一个生成问题，需要对上下文条件进行语义理解以及对连续时间动态进行随机建模。现有方法通常要么依赖自回归大语言模型进行语义上下文建模，要么依赖扩散类模型进行连续概率生成。但单独一种方法都无法同时充分对这两方面建模。本文提出CoGenCast，一种将预训练大语言模型与流匹配机制相结合的混合生成框架用于有效时间序列预测。具体通过仅修改注意力拓扑将预训练仅解码器大语言模型重新配置成本地预测编码器 - 解码器主干，实现双向上下文编码和因果表示生成。在此基础上，进一步集成流匹配机制来建模时间演变，捕捉基于自回归生成表示的连续随机动态。CoGenCast自然支持多模态预测和跨域统一训练。在多个基准上的大量实验表明，CoGenCast与先前基线相比具有有竞争力的性能。
英文摘要：
Time series forecasting can be viewed as a generative problem that requires both semantic understanding over contextual conditions and stochastic modeling of continuous temporal dynamics. Existing approaches typically rely on either autoregressive large language models (LLMs) for semantic context modeling or diffusion-like models for continuous probabilistic generation. However, neither method alone can adequately model both aspects simultaneously. In this work, we propose CoGenCast, a hybrid generative framework that couples pre-trained LLMs with flow-matching mechanism for effective time series forecasting. Specifically, we reconfigure pre-trained decoder-only LLMs into a native forecasting encoder-decoder backbone by modifying only the attention topology, enabling bidirectional context encoding and causal representation generation. Building on this, a flow-matching mechanism is further integrated to model temporal evolution, capturing continuous stochastic dynamics conditioned on the autoregressively generated representation. Notably, CoGenCast naturally supports multimodal forecasting and cross-domain unified training. Extensive experiments on multiple benchmarks show that CoGenCast achieves competitive performance compared to previous baselines. Code is available at https://github.com/liuyaguo/_CoGenCast.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
