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

2026-03-25

AI Summary

This work addresses the tendency of existing end-to-end time series forecasting methods to overlook extreme yet informative patterns while optimizing for average error, often yielding overly smoothed predictions. To mitigate this limitation, the authors propose ReGuider, a novel approach that leverages intermediate embeddings from a pretrained time series foundation model as semantic teacher signals. These signals enable plug-and-play supervised training of the encoder in any forecasting architecture at the representation level, facilitating semantic alignment and enhanced feature representation. Extensive experiments demonstrate that ReGuider consistently improves forecasting performance across diverse datasets and model architectures, underscoring its effectiveness and broad applicability.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

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 improving the accuracy of downstream forecasting. Extensive experimentation across diverse datasets and architectures demonstrates that our ReGuider consistently improves forecasting performance, confirming its effectiveness and versatility.

Problem

• time series forecasting
• representation learning
• extreme patterns
• temporal dynamics
• encoder representation

Innovation

• representation-level supervision
• time series forecasting
• foundation models
• plug-in method
• temporal representation learning