Study examines parameter-efficient tuning of language models for time-series forecasting

A new arXiv paper investigates how pretrained language models can be adapted for univariate time-series forecasting using parameter-efficient transfer learning. The authors focus on identifying which design decisions matter most for effective transfer between text and numerical sequences. The work is a cross-listed submission to arXiv's machine learning category.