GANterpolate+: A Unified Framework for Data Reconstruction in Sparse and Heterogeneous Domains

Reconstructing incomplete or irregular time series remains a persistent challenge across scientific, industrial, and operational domains, where missing observations can hinder accurate modeling, forecasting, and decision-making. Building on our earlier GANterpolate framework—which integrates Generative Adversarial Networks with interpolation refinement—this work introduces GANterpolate+, a generalized and domain-adaptive architecture designed for high-fidelity reconstruction in heterogeneous spatiotemporal datasets. The framework incorporates a domain-aware consistency objective to stabilize adversarial-learning under varying data regimes, along with spatiotemporal embedding layers that capture structural, temporal, and contextual dependencies without relying on domain-specific assumptions. To evaluate its cross-domain adaptability, GANterpolate+ is applied to multiple real-world settings characterized by data sparsity, distributional shifts, and irregular sampling patterns. Experimental results demonstrate that GANterpolate+ consistently surpasses interpolation-only and GAN-only baselines across both random and clustered missingness, achieving improved reconstruction accuracy, stability, and multivariate coherence. Beyond accuracy gains, the unified and modular design enables straightforward extension to new data modalities with minimal reconfiguration. These findings establish GANterpolate+ as a scalable, robust, and versatile hybrid solution for recovering high-quality information from incomplete spatiotemporal observations across diverse application domains.