Forecasts.CF: one-step ahead forecast by separate time series analysis

Description Usage Arguments Value Author(s) References

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

1
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.


COST documentation built on May 2, 2019, 9:33 a.m.

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