tecov {FoReco} | R Documentation |
Temporal covariance matrix approximation
Description
This function provides an approximation of the temporal base forecasts errors covariance matrix using different reconciliation methods (see Di Fonzo and Girolimetto, 2023).
Usage
tecov(comb, agg_order = NULL, res = NULL, tew = "sum",
mse = TRUE, shrink_fun = shrink_estim, ...)
Arguments
comb |
A string specifying the reconciliation method.
|
agg_order |
Highest available sampling frequency per seasonal cycle (max. order
of temporal aggregation, |
res |
A ( |
tew |
A string specifying the type of temporal aggregation. Options include:
" |
mse |
If |
shrink_fun |
Shrinkage function of the covariance matrix, shrink_estim (default) |
... |
Not used. |
Value
A ((k^\ast+m) \times (k^\ast+m)
) symmetric matrix.
References
Di Fonzo, T. and Girolimetto, D. (2023), Cross-temporal forecast reconciliation: Optimal combination method and heuristic alternatives, International Journal of Forecasting, 39, 1, 39-57. doi:10.1016/j.ijforecast.2021.08.004
See Also
Temporal framework:
teboot()
,
tebu()
,
telcc()
,
temo()
,
terec()
,
tetd()
,
tetools()
Examples
# (7 x 70) in-sample residuals matrix (simulated), agg_order = 4
res <- rnorm(70)
cov1 <- tecov("ols", agg_order = 4) # OLS methods
cov2 <- tecov("str", agg_order = 4) # STRC methods
cov3 <- tecov("wlsv", agg_order = 4, res = res) # WLSv methods
cov4 <- tecov("wlsh", agg_order = 4, res = res) # WLSh methods
cov5 <- tecov("acov", agg_order = 4, res = res) # ACOV methods
cov6 <- tecov("strar1", agg_order = 4, res = res) # STRAR1 methods
cov7 <- tecov("har1", agg_order = 4, res = res) # HAR1 methods
cov8 <- tecov("sar1", agg_order = 4, res = res) # SAR1 methods
cov9 <- tecov("shr", agg_order = 4, res = res) # SHR methods
cov10 <- tecov("sam", agg_order = 4, res = res) # SAM methods
# Custom covariance matrix
tecov.ols2 <- function(comb, x) diag(x)
tecov(comb = "ols2", x = 7) # == tecov("ols", agg_order = 4)