Medical irregular multivariate time series forecasting based on multi-scale temporal-frequency domain patch fusion and dynamic graph

Introduction: Accurate forecasting of medical irregular multivariate time series is an important prerequisite for downstream monitoring and decision-support research. However, this task remains challenging because physiological data are typically characterized by irregular sampling, missing values, and complex temporal and inter-variable dependencies. Methods: To address these challenges, we propose a novel method termed Multi-scale Temporal-Frequency domain fusion Patching and Dynamic Graph modeling (MTFP-DG). The method first transforms irregular time series into multi-scale patches with unified temporal resolution, enabling temporal alignment without interpolation and thereby handling irregularity and asynchrony. It then employs a dual-domain encoding mechanism that fuses temporal features extracted by a Transformable Time-aware Convolution Network with frequency features extracted by an Irregular Fourier Analysis Network to obtain rich patch representations. Based on these representations and Fourier coefficients, dynamic graphs are further constructed to capture evolving inter-variable correlations. Results: Extensive experiments on five real-world medical datasets demonstrate that MTFP-DG outperforms state-of-the-art baselines on retrospective irregular multivariate time series forecasting benchmarks. Discussion: These findings indicate that integrating multi-scale patching with dynamic graph modeling is effective for capturing complex temporal dependencies and inter-series relationships in medical irregular multivariate time series. MTFP-DG may provide a robust methodological tool for proactive healthcare planning, although its clinical utility still requires further prospective validation.