In-context Time Series Predictor
<|reference_start|>In-context Time Series Predictor: Recent Transformer-based large language models (LLMs) demonstrate in-context learning ability to perform various functions based solely on the provided context, without updating model parameters. To fully utilize the in-context capabilities in time series forecasting (TSF) problems, unlike previous Transformer-based or LLM-based time series forecasting methods, we reformulate "time series forecasting tasks" as input tokens by constructing a series of (lookback, future) pairs within the tokens. This method aligns more closely with the inherent in-context mechanisms, and is more parameter-efficient without the need of using pre-trained LLM parameters. Furthermore, it addresses issues such as overfitting in existing Transformer-based TSF models, consistently achieving better performance across full-data, few-shot, and zero-shot settings compared to previous architectures.<|reference_end|>
@article{lu2024in-context,
    title={In-context Time Series Predictor},
    author={Jiecheng Lu, Yan Sun, Shihao Yang},
    journal={arXiv preprint arXiv:2405.14982},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14982},
    primaryClass={cs.LG cs.AI cs.CL stat.ML}
}