Temporal probabilistic reconciliation (sample approach)

Usage

tesample(sample, agg_order, fun = terec, ...)

Arguments

sample: A (\(L \times h(k^\ast + m)\)) numeric matrix containing the base forecasts samples to be reconciled; \(m\) is the max aggregation order, \(k^\ast\) is the sum of (a subset of) (\(p-1\)) factors of \(m\), excluding \(m\), \(h\) is the forecast horizon for the lowest frequency time series, and \(L\) is the sample size.
agg_order: Highest available sampling frequency per seasonal cycle (max. order of temporal aggregation, \(m\)), or a vector representing a subset of \(p\) factors of \(m\).
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)
m <- 4 # from quarterly to annual temporal aggregation

# (100 x 14) base forecasts sample matrix (simulated), m = 4, h = 2
sample <- t(sapply(1:100, function(x) rnorm(14, rep(c(20, 10, 5), 2*c(1, 2, 4)))))
# (70 x 1) in-sample residuals vector (simulated)
res <- rnorm(70)

# Optimal cross-sectional probabilistic reconciliation
reco_dist_opt <- tesample(sample, agg_order = m, res = res, comb = "shr")

# Bottom-up probabilistic reconciliation
reco_dist_bu <- tesample(sample[,-c(1:6)], agg_order = m, fun = tebu)

# Level conditional coherent probabilistic reconciliation
reco_dist_lcc <- tesample(sample, agg_order = m, fun = telcc)