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The cross-temporal middle-out forecast reconciliation combines top-down ([cttd]) and bottom-up ([ctbu]) methods in the cross-temporal framework for genuine hierarchical/grouped time series. Given the base forecasts of an intermediate cross-sectional level \(l\) and aggregation order \(k\), it performs

  • a top-down approach for the aggregation orders \(\geq k\) and cross-sectional levels \(\geq l\);

  • a bottom-up approach, otherwise.

Usage

ctmo(base, agg_mat, agg_order, id_rows = 1, order = max(agg_order),
     weights, tew = "sum", normalize = TRUE)

Arguments

base

A (\(n_l \times hk\)) numeric matrix containing the \(l\)-level base forecasts of temporal aggregation order \(k\); \(n_l\) is the number of variables at level \(l\), \(k\) is an aggregation order (a factor of \(m\), and \(1<k<m\)), \(m\) is the max aggregation order, and \(h\) is the forecast horizon for the lowest frequency time series.

agg_mat

A (\(n_a \times n_b\)) numeric matrix representing the cross-sectional aggregation matrix. It maps the \(n_b\) bottom-level (free) variables into the \(n_a\) upper (constrained) variables.

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 \(m\).

id_rows

A numeric vector indicating the \(l\)-level rows of agg_mat.

order

The intermediate fixed aggregation order \(k\).

weights

A (\(n_b \times hm\)) numeric matrix containing the proportions for each high-frequency bottom time series; \(n_b\) is the total number of high-frequency bottom variables, \(m\) 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.

Value

A (\(n \times h(k^\ast+m)\)) numeric matrix of cross-temporal reconciled forecasts.

See also

Middle-out reconciliation: csmo(), temo()

Cross-temporal framework: ctboot(), ctbu(), ctcov(), ctlcc(), ctrec(), cttd(), cttools(), iterec(), tcsrec()

Examples

set.seed(123)
# Aggregation matrix for Z = X + Y, X = XX + XY and Y = YX + YY
A <- matrix(c(1,1,1,1,1,1,0,0,0,0,1,1), 3, byrow = TRUE)
# (2 x 6) base forecasts matrix (simulated), forecast horizon = 3
# and intermediate aggregation order k = 2 (max agg order = 4)
baseL2k2 <- rbind(rnorm(3*2, 5), rnorm(3*2, 5))

# Same weights for different forecast horizons, agg_order = 4
fix_weights <- matrix(runif(4*4), 4, 4)
reco <- ctmo(base = baseL2k2, id_rows = 2:3, agg_mat = A,
             order = 2, agg_order = 4, weights = fix_weights)

# Different weights for different forecast horizons
h_weights <- matrix(runif(4*4*3), 4, 3*4)
recoh <- ctmo(base = baseL2k2, id_rows = 2:3, agg_mat = A,
             order = 2, agg_order = 4, weights = h_weights)