Timer: Generative Pre-trained Transformers Are Large Time Series Models
<|reference_start|>Timer: Generative Pre-trained Transformers Are Large Time Series Models: Deep learning has contributed remarkably to the advancement of time series analysis. Still, deep models can encounter performance bottlenecks in real-world data-scarce scenarios, which can be concealed due to the performance saturation with small models on current benchmarks. Meanwhile, large models have demonstrated great powers in these scenarios through large-scale pre-training. Continuous progress has been achieved with the emergence of large language models, exhibiting unprecedented abilities such as few-shot generalization, scalability, and task generality, which are however absent in small deep models. To change the status quo of training scenario-specific small models from scratch, this paper aims at the early development of large time series models (LTSM). During pre-training, we curate large-scale datasets with up to 1 billion time points, unify heterogeneous time series into single-series sequence (S3) format, and develop the GPT-style architecture toward LTSMs. To meet diverse application needs, we convert forecasting, imputation, and anomaly detection of time series into a unified generative task. The outcome of this study is a Time Series Transformer (Timer), which is generative pre-trained by next token prediction and adapted to various downstream tasks with promising capabilities as an LTSM. Code and datasets are available at: https://github.com/thuml/Large-Time-Series-Model.<|reference_end|>
@article{liu2024timer:,
    title={Timer: Generative Pre-trained Transformers Are Large Time Series Models},
    author={Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang,
  Mingsheng Long},
    journal={arXiv preprint arXiv:2402.02368},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02368},
    primaryClass={cs.LG stat.ML}
}