tetd function

Temporal top-down reconciliation

Top-down forecast reconciliation for a univariate time series, where the forecast of the most aggregated temporal level is disaggregated according to a proportional scheme (weights). Besides fulfilling any aggregation constraint, the top-down reconciled forecasts should respect two main properties:

• the top-level value remains unchanged;
• all the bottom time series reconciled forecasts are non-negative.

tetd(base, agg_order, weights, tew = "sum", normalize = TRUE)

Arguments

• base: A ($hm \times 1$) numeric vector containing the temporal aggregated base forecasts of order $m$; $m$ is the max aggregation order, and $h$ is the forecast horizon for the lowest frequency time series.
• 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 $m$.
• weights: A ($hm \times 1$) numeric vector containing the proportions for the high-frequency time series; $m$ is the max aggregation order, and $h$ is the forecast horizon for the lowest frequency time series.
• tew: A string specifying the type of temporal aggregation. Options include: "sum" (simple summation, default), "avg" (average), "first" (first value of the period), and "last" (last value of the period).
• normalize: If TRUE (default), the weights will sum to 1.

Returns

A ($h(k^\ast+m) \times 1$) numeric vector of temporal reconciled forecasts.

Examples

set.seed(123)
# (2 x 1) top base forecasts vector (simulated), forecast horizon = 2
topf <- rnorm(2, 10)
# Same weights for different forecast horizons
fix_weights <- runif(4)
reco <- tetd(base = topf, agg_order = 4, weights = fix_weights)

# Different weights for different forecast horizons
h_weights <- runif(4*2)
recoh <- tetd(base = topf, agg_order = 4, weights = h_weights)

See Also

Top-down reconciliation: cstd(), cttd()

Temporal framework: teboot(), tebu(), tecov(), telcc(), temo(), terec(), tetools()