example.forecast: example for one-step ahead forecast

In COST: Copula-Based Semiparametric Models for Spatio-Temporal Data

Description

Example for one-step ahead forecast for Gaussian Process and our COST method with Gaussian and t copulas, where the data are generated from COST DGP, where the parameters are assumed to be known; the parameters can be obtained by the “optim" function. Assuming that data are observed at d=9 locations, and n+1 time points, where the last time point is for validation.

Usage

example.forecast(n,n.total,seed1)

Arguments

n	number of time points for parameter estimation
n.total	number of total time points, with a burning sequence
seed1	random seed to generate a data set, for reproducibility

Value

COST.t.fore.ECP	a vector of length d, with value 1 or 0, 1 means the verifying value from the corresponding location lies in the 95% forecast interval, 0 means not
COST.t.fore.ML	a vector of length d, each element is the length of forecast interval of the corresponding location
COST.t.fore.rank	multivariate rank of the verifying vector by t copula
COST.G.fore.ECP	same as COST.t.fore.ECP
COST.G.fore.ML	same as COST.t.fore.ML
COST.G.fore.rank	multivariate rank of the verifying vector by Gaussian copula
GP.fore.ECP	same as COST.t.fore.ECP
GP.fore.ML	same as COST.t.fore.ML
GP.fore.rank	multivariate rank of the verifying vector by Gaussian process method

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.

Examples

library(COST)
#settings
seed1 = 2222222
n.total = 101 #number of total time points, including the burning sequence
n = 50 #number of time points we observed
example.forecast(n,n.total,seed1)
#OUTPUTS

# $COST.t.fore.ECP #whether the forecast interval includes the true value at n+1
# [1] 1 1 1 1 1 1 1 1 1
#
# $COST.t.fore.ML #length of the forecast interval
# [1] 0.7036 4.1318 4.8749 2.7615 3.7398 5.8186 4.4532 4.9251 6.3757
#
# $COST.t.fore.rank #multivariate rank
# [1] 162
#
#
# $COST.G.fore.ECP #whether the forecast interval includes the true value at n+1
# [1] 1 1 1 1 1 1 1 1 1
#
# $COST.G.fore.ML #length of the forecast interval
# [1]  0.7035 4.1316 4.8656 2.7611 3.7388 5.7913 4.4458 4.9036 6.3727
#
# $COST.G.fore.rank #multivariate rank
# [1] 186
#

# $GP.fore.ECP #whether the forecast interval includes the true value at n+1
# [1] 1 0 0 1 1 1 1 1 1
#
# $GP.fore.ML #length of the forecast interval
# [1] 0.4879 2.0449 3.4436 2.2107 2.9170 4.4537 4.2169 5.5789 7.3689
#
# $GP.fore.rank #multivariate rank
# [1] 17

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