SOURCE-LINKED INTELLIGENCE

Parameter-Efficient Adaptation of Pretrained Language Models for Time-Series Forecasting

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

We study the adaptation of pretrained language models to univariate time-series forecasting through a parameter-efficient transfer learning framework, with the goal of understanding which design choices drive effective cross-modal transfer. While language models operate on discrete textual tokens, time series consist of continuous numerical observations with temporal dependencies. To bridge this modality gap, we project fixed-length time-series patches directly into the embedding space of a pretrained GPT-2 backbone, bypassing textual tokenization and treating the Transformer as a generic sequ

Read original source ↗ Open in workspace

recordType

paper

region

Global