one-step ahead forecast by Gaussian process fitting, including: (i) point forecast, either conditional mean; (ii) 95% forecast intervals, which can also be adjusted by the users; (iii) m (m=500 by default) random draws from the conditional distribution, can be used for multivariate rank
Forecasts.GP(par,Y,s.ob,seed1,m,isotropic)parameters in the copula function
observed data
coordinates of observed locations
random seed used to generate random draws from the conditional distribution, for reproducibility
number of random draws to approximate the conditional distribution
indicator, True for isotropic correlation matrix, False for anisotropic correlation matrix, and we usually choose False for flexibility
0.025-, 0.975- and 0.5-th conditional quantiles of the conditional distribution for each location
conditional mean estimate for each location
m random draws from the conditional distribution
Yanlin Tang, Huixia Judy Wang, Ying Sun, Amanda Hering. Copula-based semiparametric models for spatio-temporal data.