A parsimonious, computationally efficient machine learning method for spatial regression

Abstract

AbstractWe introduce the modified planar rotator method (MPRS), a physically inspired machine learning method for spatial/temporal regression. MPRS is a non-parametric model which incorporates spatial or temporal correlations via short-range, distance-dependent “interactions” without assuming a specific form for the underlying probability distribution. Predictions are obtained by means of a fully autonomous learning algorithm which employs equilibrium conditional Monte Carlo simulations. MPRS is able to handle scattered data and arbitrary spatial dimensions. We report tests on various synthetic and real-word data in one, two and three dimensions which demonstrate that the MPRS prediction performance (without hyperparameter tuning) is competitive with standard interpolation methods such as ordinary kriging and inverse distance weighting. MPRS is a particularly effective gap-filling method for rough and non-Gaussian data (e.g., daily precipitation time series). MPRS shows superior computational efficiency and scalability for large samples. Massive datasets involving millions of nodes can be processed in a few seconds on a standard personal computer. We also present evidence that MPRS, by avoiding the Gaussian assumption, provides more reliable prediction intervals than kriging for highly skewed distributions.