Pattern-Guided Diffusion Models

2025-12-15

AI Summary

Existing diffusion models for multivariate time series forecasting commonly neglect recurrent temporal patterns in the data, resulting in generated forecasts that lack structural coherence and robustness. To address this, we propose the first pattern-aware forecasting framework that integrates archetypal analysis with diffusion modeling. Our method employs unsupervised archetype discovery to extract prototypical temporal representations capturing intrinsic recurring structures, and introduces an uncertainty-driven adaptive guidance mechanism that dynamically modulates the influence of these patterns during the denoising process. Evaluated on two real-world tasks—visual field progression monitoring and motion capture—we achieve substantial improvements: MAE and CRPS metrics improve by up to 56.26%, with average gains of 84.83% over state-of-the-art baselines. These results empirically validate the critical importance of explicitly modeling recurrent temporal patterns in diffusion-based forecasting.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Time-Series/Data Streams
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Diffusion models have shown promise in forecasting future data from multivariate time series. However, few existing methods account for recurring structures, or patterns, that appear within the data. We present Pattern-Guided Diffusion Models (PGDM), which leverage inherent patterns within temporal data for forecasting future time steps. PGDM first extracts patterns using archetypal analysis and estimates the most likely next pattern in the sequence. By guiding predictions with this pattern estimate, PGDM makes more realistic predictions that fit within the set of known patterns. We additionally introduce a novel uncertainty quantification technique based on archetypal analysis, and we dynamically scale the guidance level based on the pattern estimate uncertainty. We apply our method to two well-motivated forecasting applications, predicting visual field measurements and motion capture frames. On both, we show that pattern guidance improves PGDM's performance (MAE / CRPS) by up to 40.67% / 56.26% and 14.12% / 14.10%, respectively. PGDM also outperforms baselines by up to 65.58% / 84.83% and 93.64% / 92.55%.

Problem

• Leveraging recurring patterns in multivariate time series for forecasting
• Guiding diffusion models with extracted patterns to improve prediction realism
• Introducing pattern-based uncertainty quantification and dynamic guidance scaling

Innovation

• Pattern-guided diffusion models leverage recurring temporal patterns for forecasting
• Archetypal analysis extracts patterns and estimates the most likely next pattern
• Dynamic guidance scaling based on pattern uncertainty improves prediction realism