PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Medical Devices & Health Technology · Depth: Expert, extended

Summary

PAMF is a multimodal time-series framework designed to address incomplete observations in healthcare data, which commonly exhibit within-modality missing (values absent within an observed modality) and modality-level missing (entire modality unavailable). Existing methods often treat missingness uniformly or only handle one pattern. PAMF explicitly estimates missing data by coupling imputation with downstream prediction through prior-aware flow matching and weight sharing. It initializes flow-matching source states with type-specific priors to distinguish missing types and connects imputation and classification via architecturally matched encoders with weight sharing. Experiments on four multimodal healthcare time-series benchmarks, including Sleep-EDF, PTB-XL, PPG-DaLiA, and Chapman-Shaoxing, demonstrate that PAMF achieves the strongest overall downstream performance across diverse datasets and missing settings, including 20% and 50% missing rates, compared to existing baselines.

Key takeaway

For machine learning engineers developing multimodal healthcare applications with incomplete time series data, PAMF offers a robust solution. Its explicit handling of distinct missingness patterns and integrated imputation with downstream tasks significantly improves predictive performance. You should consider adopting prior-aware flow matching and the three-phase training strategy to enhance model resilience and accuracy against data gaps, particularly in clinical settings where data completeness is a persistent challenge.

Key insights

PAMF explicitly handles distinct missingness patterns and couples imputation with downstream tasks for superior multimodal time-series analysis.

Principles

• Missing data requires type-specific imputation strategies.
• Imputation and downstream tasks benefit from coupled learning.
• Flow matching with prior-aware initialization reduces inference steps.

Method

PAMF uses prior-aware flow matching with type-specific initializations (temporal neighbor for within-modality, batch mean for modality-level) and weight-sharing via a three-phase training strategy to connect imputation and downstream prediction.

In practice

• Distinguish within-modality from modality-level missing for tailored imputation.
• Initialize flow matching with structured priors to reduce transport distance.
• Employ weight sharing between imputation and prediction encoders.

Topics

• Multimodal Time Series
• Missing Data Imputation
• Flow Matching
• Prior-Aware Initialization
• Weight Sharing
• Healthcare Applications

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.