Function to calculate the accuracy indices of the reconciled point forecasts of a cross-temporal (not only, see examples) system (more in Average relative accuracy indices). (Experimental version)
score_index(recf, base, test, m, nb, nl, type = "mse", compact = TRUE)list of q (forecast origins) reconciled forecasts' matrices (\([n \times h(k^\ast + m)]\) in the cross-temporal case, \([h \times n]\) in the cross-sectional case, and vectors of length \([h(k^\ast \times m)]\) in the temporal framework).
list of q (forecast origins) base forecasts' matrices (\([n \times h(k^\ast + m)]\) in the cross-temporal case, \([h \times n]\) in the cross-sectional case, and vectors of length \([h(k^\ast \times m)]\) in the temporal framework).
list of q (forecast origins) test observations' matrices (\([n \times h(k^\ast + m)]\) in the cross-temporal case, \([h \times n]\) in the cross-sectional case, and vectors of length \([h(k^\ast \times m)]\) in the temporal framework).
Highest available sampling frequency per seasonal cycle (max. order of temporal aggregation, \(m\)), or a subset of \(p\) factors of \(m\).
number of bottom time series in the cross-sectional framework.
(\(L \times 1\)) vector containing the number of time series
in each cross-sectional level of the hierarchy (nl[1] = 1).
type of accuracy measure ("mse" Mean Square Error,
"rmse" Root Mean Square Error or "mae" Mean Absolute Error).
if TRUE returns only the summary matrix.
It returns a summary table called Avg_mat (if compact option is TRUE,
default), otherwise it returns a list of six tables (more in
Average relative accuracy indices).
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.
Other utilities:
Cmatrix(),
FoReco2ts(),
agg_ts(),
arrange_hres(),
commat(),
ctf_tools(),
hts_tools(),
lcmat(),
oct_bounds(),
residuals_matrix(),
shrink_estim(),
thf_tools()
# \donttest{
data(FoReco_data)
# Cross-temporal framework
oct_recf <- octrec(FoReco_data$base, m = 12, C = FoReco_data$C,
comb = "bdshr", res = FoReco_data$res)$recf
oct_score <- score_index(recf = oct_recf,
base = FoReco_data$base,
test = FoReco_data$test, m = 12, nb = 5)
# Cross-sectional framework
# monthly base forecasts
mbase <- FoReco2matrix(FoReco_data$base, m = 12)$k1
# monthly test set
mtest <- FoReco2matrix(FoReco_data$test, m = 12)$k1
# monthly residuals
mres <- FoReco2matrix(FoReco_data$res, m = 12)$k1
# monthly reconciled forecasts
mrecf <- htsrec(mbase, C = FoReco_data$C, comb = "shr", res = mres)$recf
# score
hts_score <- score_index(recf = mrecf, base = mbase, test = mtest, nb = 5)
# Temporal framework
# top ts base forecasts ([lowest_freq' ... highest_freq']')
topbase <- FoReco_data$base[1, ]
# top ts residuals ([lowest_freq' ... highest_freq']')
topres <- FoReco_data$res[1, ]
# top ts test ([lowest_freq' ... highest_freq']')
toptest <- FoReco_data$test[1, ]
# top ts recf ([lowest_freq' ... highest_freq']')
toprecf <- thfrec(topbase, m = 12, comb = "acov", res = topres)$recf
# score
thf_score <- score_index(recf = toprecf, base = topbase, test = toptest, m = 12)
# }