Extract the Reconciled Model from a Reconciliation Results — 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

An rml_fit object (S3 class) that extends the reconciled model(s) with reconciliation metadata. This object lets you pre-train the reconciliation approach before base forecasts are available: the fitted result can then be passed to the fit argument of csrml, terml or ctrml to reconcile new forecasts without refitting. While the underlying list of models can be retrieved by extracting the fit element, the object is primarily intended to be used as-is.

Examples

# \donttest{
# 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: reconciled forecasts matrix
reco <- csrml(base = base, hat = hat, obs = obs, agg_mat = agg_mat)

mdl <- extract_reconciled_ml(reco)
mdl
#> <rml_fit: 2 models, cross-sectional framework>
#>  ├─ B: <randomForest>
#>  └─ C: <randomForest>
summary(mdl)
#> ℹ Cross-sectional reconciliation using Machine Learning methods
#> • Machine Learning approach: `randomForest`
#> • Number of cross-sectional series: 3
#> • Number of features: 3
#> • Training sample size: 100
#> 
#> ── Cross-sectional linear combination matrix 
#> 1 x 2 sparse Matrix of class "dgCMatrix"
#>   B C
#> A 1 1
#> 
#> ── Trained models 
#> <rml_fit: 2 models, cross-sectional framework>
#>  ├─ B: <randomForest>
#>  └─ C: <randomForest>
# }