Extract the reconciled model from a reconciliation result — extract_reconciled

Extract the fitted reconciled model(s) from a reconciliation function's output (e.g., csrml, terml and ctrml). The model can be reused for forecast reconciliation in the reconciliation functions.

Usage

extract_reconciled_ml(reco)

Arguments

reco: An object returned by a reconciliation function (e.g., the result of csrml, terml and ctrml).

Value

A named list with reconciliation information:

sel_mat: Features used (e.g., the selected feature matrix or indices).
fit: List of reconciled models.
approach: The learning approach used (e.g., "xgboost", "lightgbm", "randomForest", "mlr3").

Examples

# agg_mat: simple aggregation matrix, A = B + C
agg_mat <- t(c(1,1))
dimnames(agg_mat) <- list("A", c("B", "C"))

# N_hat: dimension for the most aggregated training set
N_hat <- 100

# ts_mean: mean for the Normal draws used to simulate data
ts_mean <- c(20, 10, 10)

# hat: a training (base forecasts) feautures matrix
hat <- matrix(
  rnorm(length(ts_mean)*N_hat, mean = ts_mean),
  N_hat, byrow = TRUE)
colnames(hat) <- unlist(dimnames(agg_mat))

# obs: (observed) values for bottom-level series (B, C)
obs <- matrix(
  rnorm(length(ts_mean[-1])*N_hat, mean = ts_mean[-1]),
  N_hat, byrow = TRUE)
colnames(obs) <- colnames(agg_mat)

# h: base forecast horizon
h <- 2

# base: base forecasts matrix
base <- matrix(
  rnorm(length(ts_mean)*h, mean = ts_mean),
  h, byrow = TRUE)
colnames(base) <- unlist(dimnames(agg_mat))
# `reco` is the result of a reconciliation call:
reco <- csrml(base = base, hat = hat, obs = obs, agg_mat = agg_mat)

mdl <- extract_reconciled_ml(reco)
mdl
#> ----- Reconciled models -----
#> Framework: cs 
#> Features: all 
#> Approach: randomForest 
#>   Models: 2