one-step ahead forecast, analyzing the time series at each location separately with a t 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 for each location, can be used for multivariate rank after combining all the locations together
Forecasts.CF(par,Y,seed1,m)parameters in the copula function
observed data
random seed used to generate random draws from the conditional distribution, for reproducibility
number of random draws to approximate the conditional distribution
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.