Cross-sectional probabilistic reconciliation (sample approach)

Usage

cssample(sample, fun = csrec, ...)

Arguments

sample: A (\(h \times n \times L\)) numeric array containing the base forecasts samples to be reconciled; \(h\) is the forecast horizon, \(n\) is the total number of time series (\(n = n_a + n_b\)), and \(L\) is the sample size.
fun: A string specifying the reconciliation function to be used, as implemented in FoReco.
...: Arguments passed on to fun

Value

A distributional::dist_sample object.

References

Girolimetto, D., Athanasopoulos, G., Di Fonzo, T. and Hyndman, R.J. (2024), Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues. International Journal of Forecasting, 40, 3, 1134-1151. doi:10.1016/j.ijforecast.2023.10.003

Panagiotelis, A., Gamakumara, P., Athanasopoulos, G. and Hyndman, R.J. (2023), Probabilistic forecast reconciliation: Properties, evaluation and score optimisation, European Journal of Operational Research 306(2), 693–706. doi:10.1016/j.ejor.2022.07.040

Examples

set.seed(123)
A <- t(c(1,1)) # Aggregation matrix for Z = X + Y

# (100 x 3) base forecasts sample (simulated) for h = 1
base_h1 <- matrix(rnorm(100*3, mean = c(20, 10, 10)), 100, byrow = TRUE)
# (100 x 3) base forecasts sample (simulated) for h = 2
base_h2 <- matrix(rnorm(100*3, mean = c(20, 10, 10)), 100, byrow = TRUE)
# (2 x 3 x 100) base forecasts sample array with
# 2 forecast horizons, 3 time series and 100 sample
base_sample <- aperm(simplify2array(list(base_h1, base_h2)), c(3,2,1))

# Optimal cross-sectional probabilistic reconciliation
reco_dist_opt <- cssample(base_sample, agg_mat = A)

# Bottom-up probabilistic reconciliation
reco_dist_bu <- cssample(base_sample[,-1,], agg_mat = A, fun = csbu)

# Level conditional coherent probabilistic reconciliation
reco_dist_lcc <- cssample(base_sample, agg_mat = A, fun = cslcc)