Time series forecasting holds significant importance in many real-world
dynamic systems and has been extensively studied. Unlike natural language
process (NLP) and computer vision (CV), where a single large model can tackle
multiple tasks, models for time series forecasting are often specialized,
necessitating distinct designs for different tasks and applications. While
pre-trained foundation models have made impressive strides in NLP and CV, their
development in time series domains has been constrained by data sparsity.
Recent studies have revealed that large language models (LLMs) possess robust
pattern recognition and reasoning abilities over complex sequences of tokens.
However, the challenge remains in effectively aligning the modalities of time
series data and natural language to leverage these capabilities. In this work,
we present Time-LLM, a reprogramming framework to repurpose LLMs for general
time series forecasting with the backbone language models kept intact. We begin
by reprogramming the input time series with text prototypes before feeding it
into the frozen LLM to align the two modalities. To augment the LLM's ability
to reason with time series data, we propose Prompt-as-Prefix (PaP), which
enriches the input context and directs the transformation of reprogrammed input
patches. The transformed time series patches from the LLM are finally projected
to obtain the forecasts. Our comprehensive evaluations demonstrate that
Time-LLM is a powerful time series learner that outperforms state-of-the-art,
specialized forecasting models. Moreover, Time-LLM excels in both few-shot and
zero-shot learning scenarios.

利用大型语言模型进行时间序列预测的重新编程框架，通过在输入时间序列中加入文本原型，将两种模态对齐，进而获得强大的时间序列学习器 Time-LLM，该学习器在少样本和零样本学习场景中表现出优越性能并超越了专业的预测模型。