sim_data | R Documentation |
Generate simulated data in the form of structural time series
sim_data( X, beta, cov, k, mu, rho, mean_trend = 1, sd_trend = 0.5, mean_season = 20, sd_season = 0.5, mean_cycle = 20, sd_cycle = 0.5, Dtilde, Season, vrho, lambda ) ## S4 method for signature 'array' sim_data( X, beta, cov, k, mu, rho, mean_trend = 1, sd_trend = 0.5, mean_season = 20, sd_season = 0.5, mean_cycle = 20, sd_cycle = 0.5, Dtilde, Season, vrho, lambda )
X |
A (n*K)-dimensional matrix containing predictors, where n is the number of observations. K=∑ k_i is the number of all candidate predictors for all target series. The first k_1 variables are the set of candidate predictors for the first target series, and the next k_2 variables are the set of candidate predictors for the second target series, etc. |
beta |
A (K*m)-dimensional matrix containing all candidate predictor series for each target series. |
cov |
A (m*m)-dimensional matrix containing covariances |
k |
A m-dimensional array containing the number of candidate predictors for each of the m target series. |
mu |
A m-dimensional array with 1 representing modeling with trend for this target time series. |
rho |
A m-dimensional array representing the learning rates at which the local trend is updated. |
mean_trend |
A numerical value standing for the mean of the error term of the trend component. The default value is 1. |
sd_trend |
A numerical value standing for the standard deviation of the error term of the trend component. The default value is 0.5. |
mean_season |
A numerical value standing for the mean of the error term of the seasonal component. The default value is 20. |
sd_season |
A numerical value standing for the standard deviation of the error term of the seasonal component. The default value is 0.5. |
mean_cycle |
A numerical value standing for the mean of the error term of the cycle component. The default value is 20. |
sd_cycle |
A numerical value standing for the standard deviation of the error term of the cycle component. The default value is 0.5. |
Dtilde |
A m-dimensional array with 1 representing level in the trend component. |
Season |
A m-dimensional array indicating the seasonality for each target series, such as c(12,0). |
vrho |
A m-dimensional array of the decay value parameter of the cycle component for each target series, such as c(0,0.99). |
lambda |
A m-dimensional array of the frequence parameter of the cycle component for each target series, such as c(0,pi/100). |
Jinwen Qiu qjwsnow_ctw@hotmail.com Ning Ning patricianing@gmail.com
2018
\Ning2021
\Jammalamadaka2019
###############Setup########### n<-505 #n: sample size m<-2 #m: dimension of target series cov<-matrix(c(1.1,0.7,0.7,0.9), nrow=2, ncol=2) #covariance matrix of target series ###############Regression component########### #coefficients for predictors beta<-t(matrix(c(2,-1.5,0,4,2.5,0,0,2.5,1.5,-1,-2,0,0,-3,3.5,0.5),nrow=2,ncol=8)) set.seed(100) X1<-rnorm(n,5,5^2) X4<-rnorm(n,-2,5) X5<-rnorm(n,-5,5^2) X8<-rnorm(n,0,100) X2<-rpois(n, 10) X6<-rpois(n, 15) X7<-rpois(n, 20) X3<-rpois(n, 5) X<-cbind(X1,X2,X3,X4,X5,X6,X7,X8) ###############Simulated data################ set.seed(100) data=sim_data(X=X, beta=beta, cov, k=c(8,8), mu=c(1,1), rho=c(0.6,0.8), Dtilde=c(-1,3), Season=c(100,0), vrho=c(0,0.99), lambda=c(0,pi/100))
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