AutoTimes: Autoregressive Time Series Forecasters via Large Language
  Models
Yong Liu Guo Qin Xiangdong Huang Jianmin Wang Mingsheng Long
Foundation models of time series have not been fully developed due to the
limited availability of large-scale time series and the underexploration of
scalable pre-training. Based on the similar sequential structure of time series
and natural language, increasing research demonstrates the feasibility of
leveraging large language models (LLM) for time series. Nevertheless, prior
methods may overlook the consistency in aligning time series and natural
language, resulting in insufficient utilization of the LLM potentials. To fully
exploit the general-purpose token transitions learned from language modeling,
we propose AutoTimes to repurpose LLMs as Autoregressive Time series
forecasters, which is consistent with the acquisition and utilization of LLMs
without updating the parameters. The consequent forecasters can handle flexible
series lengths and achieve competitive performance as prevalent models.
Further, we present token-wise prompting that utilizes corresponding timestamps
to make our method applicable to multimodal scenarios. Analysis demonstrates
our forecasters inherit zero-shot and in-context learning capabilities of LLMs.
Empirically, AutoTimes exhibits notable method generality and achieves enhanced
performance by basing on larger LLMs, additional texts, or time series as
instructions.