[2603.24262] Forecasting with Guidance: Representation-Level Supervision for Time Series Forecasting

Computer Science > Machine Learning arXiv:2603.24262 (cs) [Submitted on 25 Mar 2026] Title:Forecasting with Guidance: Representation-Level Supervision for Time Series Forecasting Authors:Jiacheng Wang, Liang Fan, Baihua Li, Luyan Zhang View a PDF of the paper titled Forecasting with Guidance: Representation-Level Supervision for Time Series Forecasting, by Jiacheng Wang and 3 other authors View PDF HTML (experimental) Abstract:Nowadays, time series forecasting is predominantly approached through the end-to-end training of deep learning architectures using error-based objectives. While this is effective at minimizing average loss, it encourages the encoder to discard informative yet extreme patterns. This results in smooth predictions and temporal representations that poorly capture salient dynamics. To address this issue, we propose ReGuider, a plug-in method that can be seamlessly integrated into any forecasting architecture. ReGuider leverages pretrained time series foundation models as semantic teachers. During training, the input sequence is processed together by the target forecasting model and the pretrained model. Rather than using the pretrained model's outputs directly, we extract its intermediate embeddings, which are rich in temporal and semantic information, and align them with the target model's encoder embeddings through representation-level supervision. This alignment process enables the encoder to learn more expressive temporal representations, thereby impr...