Pattern-Guided Diffusion Models
Vivian Lin, Kuk Jin Jang, Wenwen Si, Insup Lee
TL;DR
Pattern-Guided Diffusion Models (PGDM) enhance time series forecasting by incorporating inherent recurring patterns using archetypal analysis, leading to more accurate and realistic predictions with improved uncertainty quantification.
Contribution
The paper introduces PGDM, a novel approach that leverages pattern extraction and guidance to improve diffusion-based forecasting of multivariate time series.
Findings
PGDM improves forecasting accuracy by up to 40-56% in MAE and CRPS.
Pattern guidance significantly outperforms baseline models in experiments.
Uncertainty quantification based on archetypal analysis enhances prediction reliability.
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…
Peer Reviews
Decision·Submitted to ICLR 2026
- The introduction of Archetypal Analysis for capturing inherent patterns is interesting. - The method is described clearly and is easy to implement. - The dynamic guidance scale is a thoughtful addition that enhances model performance.
- The application scope is somewhat narrow and the baseline comparisons could be more extensive. - The experiment setup is somewhat confusing. - The dynamic guidance scale is insufficiently effective.
1 The work targets recurring temporal structure and argues that guiding diffusion in a low-dimensional pattern space improves efficiency and interpretability, with a succinct, easy-to-follow pipeline. 2 The training and inference procedures are explicit; the guidance mechanism is formalized with equations; and AAUQ provides a principled, data-dependent way to scale guidance rather than relying on a fixed heuristic. 3 Bounds and geometric arguments link the uncertainty proxy to expected guidanc
1 The authors argue that AAUQ explicitly modulates guidance based on uncertainty but this paper does not report probabilistic metrics (e.g., CRPS, NLL), coverage (Prediction Interval Coverage Probability, PICP), or calibration diagnostics (e.g., reliability diagrams). 2 The attribution of gains seem ambiguous. I suggest an abalation study for the AA representation or the dynamic guidance against standard alternatives under the same backbone and compute budget,. 3 I think it would be better
1. The idea of incorporating archetypal analysis into diffusion-based forecasting is conceptually interesting and adds a degree of interpretability. 2. The paper is clearly written and well-structured; the method is presented coherently with theoretical support and illustrative figures.
1. **Insufficient experimental design.** The experimental scope is small (only two datasets in relatively narrow domains); the baselines are dated and omit stronger modern models (e.g., recent diffusion- or Transformer-based forecasters); the reason for selecting the two variants $PGDM_{MAE}$ and $PGDM_{GDE}$ are unclear; and there is a lack of comprehensive ablations to establish the contribution of key components (archetype space, AAUQ weighting, etc.). 2. **Limited analysis and discussion.**
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Taxonomy
TopicsTime Series Analysis and Forecasting · Data Visualization and Analytics · Complex Systems and Time Series Analysis