Wenbing Zhao

GraFT: Infusing Pre-trained Transformers with Relational Structure for Time Series Forecasting

Large Language Models (LLMs) have recently emerged as a leading approach for multivariate time series forecasting. However, their effectiveness is hampered by a fundamental architectural mismatch: the permutation-invariant self-attention of Transformers lacks inductive biases for the strict temporal