Forecasts.COST.t {COST}

one-step ahead forecast by t copula

Description

one-step ahead forecast by 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, can be used for multivariate rank

Usage

Forecasts.COST.t(par,Y,s.ob,seed1,m,isotropic)

Arguments

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

Value

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

Author(s)

Yanlin Tang and Huixia Judy Wang

References

Yanlin Tang, Huixia Judy Wang, Ying Sun, Amanda Hering. Copula-based semiparametric models for spatio-temporal data.