Forecasting with Guidance: Representation-Level Supervision for Time Series Forecasting

Authors: Jiacheng Wang, Liang Fan, Baihua Li, Luyan Zhang Date: 2026-03-25 Paper ID: openalex:2603.24262

Summary

The paper addresses the limitation of end-to-end time series forecasting models whose error-based objectives lead to encoders discarding extreme but informative patterns, resulting in overly smooth predictions. To mitigate this, the authors introduce ReGuider, a plug-in mechanism that integrates a pretrained time series foundation model as a semantic teacher. ReGuider extracts intermediate embeddings from the teacher and aligns them with the target model’s encoder embeddings via representation-level supervision. This guidance forces the target encoder to learn more expressive temporal representations, consistently boosting downstream forecasting accuracy across various models and datasets.

Key Contributions

• Proposed ReGuider, a plug-in method for time series forecasting that utilizes intermediate embeddings from pretrained models for supervision.
• Introduced representation-level supervision to align the target model’s encoder embeddings with those of a semantic teacher, preventing the loss of salient dynamics.
• Demonstrated consistent performance improvements across diverse time series datasets and various forecasting architectures.

Limitations

The paper relies on the availability and quality of pretrained time series foundation models to serve as semantic teachers.

Key Concepts

• reguider-representation-guidance: A plug-in method that uses representation-level supervision from a pretrained teacher model to guide the encoder training of a target time series forecasting model.
• representation-level-supervision: Supervising the intermediate encoder embeddings of a target model by aligning them with embeddings from a pretrained teacher model.

Archivist Review

The paper introduces ReGuider, a novel plug-in mechanism for time series forecasting that leverages a pretrained model as a teacher. The core reusable idea is Representation-Level Supervision, achieved by aligning intermediate encoder embeddings, which directly addresses the issue of overly smooth forecasts caused by pure error minimization. No specific datasets or substantial open questions were identified, hence only the two main technical concepts were approved based on their novelty and reusability in guiding representation learning.

Approved Concepts

• ReGuider: It is the proposed novel plug-in method that introduces representation-level supervision for improving time series forecasting encoders.
• Representation-Level Supervision: This is the core technical contribution, contrasting with standard output-level error minimization to improve learned representations.

Rejected Candidates

• [concept] Representation-Level Guidance Forecasting (representation-level-guidance-forecasting) - subcomponent_of_broader_mechanism: This concept is superseded by the specific mechanism name ‘ReGuider’ and the general technique ‘Representation-Level Supervision’.
• [concept] Representation-Level Guidance Forecasting (representation-level-guidance-forecasting) - duplicate_existing: This is a descriptive name that is covered by the approved concept ‘Representation-Level Supervision’.

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