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.

近期基于 Transformer 的大型语言模型展示了在提供的上下文情况下学习各种函数的能力，而无需更新模型参数。为了充分利用上下文能力进行时间序列预测问题，我们将 “时间序列预测任务” 重新构建为输入令牌，通过生成一系列（回顾，未来）对。该方法更加符合内在的上下文机制，并且在不使用预训练的大型语言模型参数的情况下更加参数高效。此外，它解决了现有基于 Transformer 的时间序列预测模型中的过拟合等问题，并在完整数据、少样本和零样本设置下始终比以前的架构表现更好。