A Statistical Approach for Modeling Irregular Multivariate Time Series with Missing Observations

2026-02-23

AI Summary

This study addresses the challenges of modeling irregular multivariate time series with missing values, where existing deep learning approaches often incur high computational costs and lack interpretability. The authors propose an efficient alternative that eschews complex temporal modeling by transforming the original sequences into fixed-dimensional representations through time-agnostic statistical features—such as means, standard deviations, and the mean and variability of changes between consecutive observations—combined with explicit analysis of missingness patterns. Standard classifiers, including logistic regression and XGBoost, are then applied to these representations for prediction. Evaluated on four biomedical datasets, the method achieves state-of-the-art performance, improving AUROC/AUPRC by 0.5–1.7% and accuracy/F1 by 1.1–1.7%, while substantially reducing computational complexity and offering enhanced interpretability.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Irregular multivariate time series with missing values present significant challenges for predictive modeling in domains such as healthcare. While deep learning approaches often focus on temporal interpolation or complex architectures to handle irregularities, we propose a simpler yet effective alternative: extracting time-agnostic summary statistics to eliminate the temporal axis. Our method computes four key features per variable-mean and standard deviation of observed values, as well as the mean and variability of changes between consecutive observations to create a fixed-dimensional representation. These features are then utilized with standard classifiers, such as logistic regression and XGBoost. Evaluated on four biomedical datasets (PhysioNet Challenge 2012, 2019, PAMAP2, and MIMIC-III), our approach achieves state-of-the-art performance, surpassing recent transformer and graph-based models by 0.5-1.7% in AUROC/AUPRC and 1.1-1.7% in accuracy/F1-score, while reducing computational complexity. Ablation studies demonstrate that feature extraction-not classifier choice-drives performance gains, and our summary statistics outperform raw/imputed input in most benchmarks. In particular, we identify scenarios where missing patterns themselves encode predictive signals, as in sepsis prediction (PhysioNet, 2019), where missing indicators alone can achieve 94.2% AUROC with XGBoost, only 1.6% lower than using original raw data as input. Our results challenge the necessity of complex temporal modeling when task objectives permit time-agnostic representations, providing an efficient and interpretable solution for irregular time series classification.

Problem

• irregular multivariate time series
• missing observations
• predictive modeling
• time series classification
• missing data

Innovation

• time-agnostic representation
• summary statistics
• irregular time series
• missing data patterns
• computational efficiency