TPE-GRIN: Temporal Positional Encoding Enhanced Graph Network for Missing Data Imputation in Cultural Heritage Monitoring

Missing sensor data in cultural heritage monitoring (e.g., microclimate monitoring) compromises time series continuity and threatens the assessment of critical indicators such as rock stability. Existing imputation methods often ignore spatial correlations in sensor networks and underperform in modeling nonstationary periodic patterns. To address this, we propose TPE-GRIN, a novel framework integrating Temporal Positional Encoding (TPE) with a Graph Recurrent Imputation Network (GRIN). The TPE module generates position-aware features via sine/cosine functions to adaptively fuse spatiotemporal signals for capturing diurnal/seasonal fluctuations, while the bidirectional GRIN architecture jointly models sensor topology dependencies and historical-future trends through Message-Passing Gated Units. When evaluated on the Yungang Grottoes dataset, TPE-GRIN outperforms the best baseline model GRIN in both prediction accuracy and handling long-block missing data scenarios, providing high-integrity data support for optimizing downstream sensor deployment.