Forecasts.CF {COST}

one-step ahead forecast by separate time series analysis

Description

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

Usage

Forecasts.CF(par,Y,seed1,m)

Arguments

par

parameters in the copula function

Y

observed data

seed1

random seed used to generate random draws from the conditional distribution, for reproducibility

m

number of random draws to approximate the conditional distribution

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.