Papers › AutoTimes: Autoregressive Time Series Forecasters via Large Language Models

AutoTimes: Autoregressive Time Series Forecasters via Large Language Models

4 Feb 2024arXiv:2402.02370archive 2025-07-28

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 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 fully revitalize the general-purpose token transition and multi-step generation capability of large language models, we propose AutoTimes to repurpose LLMs as autoregressive time series forecasters, which projects time series into the embedding space of language tokens 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 with larger LLMs. Further, we formulate time series as prompts, extending the context for prediction beyond the lookback window, termed in-context forecasting. By introducing LLM-embedded textual timestamps, AutoTimes can utilize chronological information to align multivariate time series. Empirically, AutoTimes achieves state-of-the-art with 0.1% trainable parameters and over 5× training/inference speedup compared to advanced LLM-based forecasters. Code is available at this repository: https://github.com/thuml/AutoTimes.

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Code

thuml/AutoTimes officialmentioned in papermentioned on GitHubpytorchMIT report

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Tasks

Decoder In-Context Learning Language Modelling Time Series Time Series Forecasting

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Time Series Forecasting	ETTh1 (336) Multivariate	AutoTimes	MAE	0.429	#6 of 72	Archive leaderboard	report
Time Series Forecasting	ETTh1 (336) Multivariate	AutoTimes	MSE	0.401	#6 of 72	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

ALIGN Adam Attention Attention Dropout BPE Cosine Annealing Dense Connections Discriminative Fine-Tuning Dropout GPT LLaMA Layer Normalization Linear Layer Linear Warmup With Cosine Annealing Multi-Head Attention Residual Connection Softmax Weight Decay

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