CRITICAL-LAYER CONTEXT ALIGNMENT FOR LARGE LANGUAGE MODEL-BASED TIME-SERIES FORECASTING

Large language models (LLMs) can forecast time series with their pretrained weights frozen. Existing approaches make numerical inputs compatible with LLMs, but leave open where to supply context and how to train for autoregressive forecasting. Similar local patterns can occur in different window states and at different times. Feeding predicted segments back into the input window also changes the history used for subsequent predictions. To use the overall state of the historical window and the corresponding time information when processing local patches, and to better adapt training to autoregressive forecasting, we propose CLCA (Critical-Layer Context Alignment). A window summary adjusts input patch embeddings, while adapters in selected layers of the fully frozen backbone combine timestamp representations with the current series state. For long-term forecasting, we feed predictions back during training and supervise subsequent segments across different forecast lengths, so that training covers the input conditions encountered during inference. Experiments on eight long-term benchmarks show overall state-of-the-art performance among the compared LLM-based and non-LLM methods under the reported protocols. Ablations show that combining the two context pathways and supervising predictions from windows containing earlier model outputs both improve forecasting performance. With adapter counts matched, adaptation in the final six layers performs best among the tested placements. Short-term, zero-shot, and alternative-backbone experiments further support the applicability of the context-adaptation design.