  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.
