  Foundation models of time series have not been fully developed due to the
limited availability of time series corpora and the underexploration of
scalable pre-training. Based on the similar sequential formulation of time
series and natural language, increasing research demonstrates the feasibility
of leveraging large language models (LLM) for time series. Nevertheless, the
inherent autoregressive property and decoder-only architecture of LLMs have not
been fully considered, resulting in insufficient utilization of LLM abilities.
To further exploit the general-purpose token transition and multi-step
generation ability of large language models, we propose AutoTimes to repurpose
LLMs as autoregressive time series forecasters, which independently projects
time series segments into the embedding space and autoregressively generates
future predictions with arbitrary lengths. Compatible with any decoder-only
LLMs, the consequent forecaster exhibits the flexibility of the lookback length
and scalability of the LLM size. Further, we formulate time series as prompts,
extending the context for prediction beyond the lookback window, termed
in-context forecasting. By adopting textual timestamps as position embeddings,
AutoTimes integrates multimodality for multivariate scenarios. Empirically,
AutoTimes achieves state-of-the-art with 0.1% trainable parameters and over 5
times training/inference speedup compared to advanced LLM-based forecasters.
