occmat {FoCo2} | R Documentation |
Matrices for the optimal coherent forecast combination
Description
This function computes the matrices required for the optimal coherent forecast combination csocc, as described in Girolimetto and Di Fonzo (2024). These matrices serve as the foundation for building forecasts that effectively combines the individual information from multiple experts while ensuring coherence across the variables.
Usage
occmat(agg_mat, cons_mat, p = NULL, matNA = NULL,
comb = "ols", res = NULL, approach = "proj", ...)
Arguments
agg_mat |
A ( |
cons_mat |
A ( |
p |
Total number of experts, |
matNA |
A ( |
comb |
A string specifying the reconciliation method. For details, see cscov. |
res |
A list of |
approach |
A string specifying the approach used to compute the reconciled forecasts. Options include:
|
... |
Arguments passed on to cscov. |
Value
A list of matrices:
M |
Projection matrix. |
Omega |
Matrix of the combination weights of the optimal linear multi-task forecast combination. |
W |
Forecast error covariance matrix of the base forecasts. |
Wc |
Forecast error covariance matrix of the combined forecasts. |
Wtilde |
Forecast error covariance matrix of the reconciled combined forecasts. |
K |
Matrix that replicates a vector (see Girolimetto and Di Fonzo, 2024). |
References
Girolimetto, D. and Di Fonzo, T. (2024), Coherent forecast combination for linearly constrained multiple time series, doi:10.48550/arXiv.2412.03429.
See Also
Other Optimal combination:
cscov()
,
csmtc()
,
csocc()