csscr {FoCo2} | R Documentation |
Cross-sectional sequential combination-reconciliation
Description
This function performs a two-step process designed to first combine forecasts from multiple models or experts and then apply reconciliation techniques to ensure coherence.
Usage
csscr(base, fc = "sa", comb = "ols", res = NULL, mse = TRUE, shrink = TRUE,
nnw = FALSE, factorized = FALSE, ...)
Arguments
base |
A list of |
fc |
A string specifying the combination method:
|
comb |
A string specifying the reconciliation method: |
res |
A list of |
mse |
If |
shrink |
If |
nnw |
If |
factorized |
Value to be passed to the |
... |
Arguments passed on to
|
Value
A (h \times n
) numeric matrix of cross-sectional combined and reconciled forecasts.
References
Bates, J. and Granger, C. W. (1969), The combination of forecasts, Operations Research Quarterly, 20, 451–468. doi:10.1057/jors.1969.103.
Conflitti, C., De Mol, C., and Giannone, D. (2015), Optimal combination of survey forecasts. International Journal of Forecasting, 31(4), 1096–1103. doi:10.1016/j.ijforecast.2015.03.009.
Girolimetto, D. and Di Fonzo, T. (2024), Coherent forecast combination for linearly constrained multiple time series. doi:10.48550/arXiv.2412.03429.
Newbold, P. and Granger, C. W. (1974), Experience with forecasting univariate time series and the combination of forecasts, Journal of the Royal Statistical Society, A, 137, 131–146. doi:10.2307/2344546
See Also
Sequential coherent combination:
cssrc()
Examples
set.seed(123)
# (2 x 3) base forecasts matrix (simulated), expert 1
base1 <- matrix(rnorm(6, mean = c(20, 10, 10)), 2, byrow = TRUE)
# (10 x 3) in-sample residuals matrix (simulated), expert 1
res1 <- t(matrix(rnorm(n = 30), nrow = 3))
# (2 x 3) base forecasts matrix (simulated), expert 2
base2 <- matrix(rnorm(6, mean = c(20, 10, 10)), 2, byrow = TRUE)
# (10 x 3) in-sample residuals matrix (simulated), expert 2
res2 <- t(matrix(rnorm(n = 30), nrow = 3))
# Base forecasts' and residuals' lists
base <- list(base1, base2)
res <- list(res1, res2)
# Aggregation matrix for Z = X + Y
A <- t(c(1,1))
reco <- csscr(base = base, agg_mat = A, comb = "wls", res = res, fc = "sa")
# Zero constraints matrix for Z - X - Y = 0
C <- t(c(1, -1, -1))
reco <- csscr(base = base, cons_mat = C, comb = "wls", res = res, fc = "sa") # same results
# Incoherent combined forecasts
fc_comb <- csscr(base = base, comb = "none", fc = "sa")
round(C %*% t(fc_comb), 3) # Incoherent forecasts