Multivariate time series forecasting has recently gained great success with the rapid growth of deep learning models. However, existing approaches usually train models from scratch using limited temporal data, preventing their generalization. Recently, with the surge of the Large Language Models (LLMs), several works have attempted to introduce LLMs into time series forecasting. Despite promising results, these methods directly take time series as the input to LLMs, ignoring the inherent modality gap between temporal and text data. In this work, we propose a novel Large Language Models and time series alignment framework, dubbed LLaTA, to fully unleash the potentials of LLMs in the time series forecasting challenge. Based on cross-modal knowledge distillation, the proposed method exploits both input-agnostic static knowledge and input-dependent dynamic knowledge in pre-trained LLMs. In this way, it empowers the forecasting model with favorable performance as well as strong generalization abilities. Extensive experiments demonstrate the proposed method establishes a new state of the art for both long- and short-term forecasting. Code is available at \url{https://github.com/Hank0626/LLaTA}.

翻译：多元时间序列预测近年来随着深度学习模型的快速发展取得了巨大成功。然而，现有方法通常使用有限的时序数据从头训练模型，限制了其泛化能力。近期，随着大语言模型（LLMs）的兴起，多项研究尝试将LLMs引入时间序列预测任务。尽管取得了令人瞩目的成果，但这些方法直接将时间序列作为LLMs的输入，忽略了时序数据与文本数据之间固有的模态差异。本文提出一种新型大语言模型与时间序列对齐框架LLaTA，旨在充分释放LLMs在时间序列预测挑战中的潜力。该方法基于跨模态知识蒸馏，同时利用预训练LLMs中与输入无关的静态知识和与输入相关的动态知识，从而赋予预测模型优异的性能与强大的泛化能力。大量实验表明，该模型在长期与短期预测任务中均创下新的最优结果。代码现已开源：\url{https://github.com/Hank0626/LLaTA}。