Similarity-Based Prediction for Digital Twins: Panel Data, Theory, and Applications

Prediction from sequential panel data is central to digital-twin modeling, where new panels arrive over time and the predictive system is updated sequentially.

Existing methods often rely on temporal proximity, which can fail when similar input-output patterns recur at nonadjacent times or when recent panels differ from the target panel.

We propose State-Local Prediction (StaLoP), a nonparametric dynamic panel prediction framework that utilizes information through target-local predictive compatibility.

StaLoP represents panels using target-local state vectors, compares historical and target panels via empirical discrepancy scores to determine relevance weights for the target point, and combines these weights with covariate localization.

Theoretical results, including bias-variance characterization, asymptotic normality, simultaneous prediction bands, and a target-local-GDF-corrected MSPE criterion for panel and model selection, are developed.

Extensive simulations validate the performance of StaLoP and support its theoretical properties.

Applications to sequence prediction, simulator calibration, variable selection, and county-to-county migration-flow forecasting demonstrate improved out-of-sample prediction and provide scientific insights into the underlying applications.