Forecasts.COST.G {COST} | R Documentation |
one-step ahead forecast by Gaussian copula, including: (i) point forecast, either conditional median or 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.COST.G(par,Y,s.ob,seed1,m,isotropic)
par |
parameters in the copula function |
Y |
observed data |
s.ob |
coordinates of observed locations |
seed1 |
random seed used to generate random draws from the conditional distribution, for reproducibility |
m |
number of random draws to approximate the conditional distribution |
isotropic |
indicator, True for isotropic correlation matrix, False for anisotropic correlation matrix, and we usually choose False for flexibility |
y.qq |
0.025-, 0.975- and 0.5-th conditional quantiles of the conditional distribution for each location |
mean.est |
conditional mean estimate for each location |
y.draw.random |
m random draws from the conditional distribution |
Yanlin Tang and Huixia Judy Wang
Yanlin Tang, Huixia Judy Wang, Ying Sun, Amanda Hering. Copula-based semiparametric models for spatio-temporal data.