Top-down forecast reconciliation for genuine hierarchical/grouped time series (Gross and Sohl, 1990), where the forecast of a Total' (top-level series, expected to be positive) 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.

## Usage

cstd(base, agg_mat, weights, normalize = TRUE)

## Arguments

base

A ($$h \times 1$$) numeric vector containing the top-level base forecast; $$h$$ is the forecast horizon.

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.

weights

A ($$h \times n_b$$) numeric matrix containing the proportions for the bottom time series; $$h$$ is the forecast horizon, and $$n_b$$ is the total number of bottom variables.

normalize

If TRUE (default), the weights will sum to 1.

## Value

A ($$h \times n$$) numeric matrix of cross-sectional reconciled forecasts.

## References

Gross, C.W. and Sohl, J.E. (1990), Disaggregation methods to expedite product line forecasting. Journal of Forecasting 9(3), 233–254. doi:10.1002/for.3980090304

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

Cross-sectional framework: csboot(), csbu(), cscov(), cslcc(), csmo(), csrec(), cstools()

## 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)
# (3 x 1) top base forecasts vector (simulated), forecast horizon = 3
topf <- rnorm(3, 10)
# Same weights for different forecast horizons
fix_weights <- runif(4)
reco <- cstd(base = topf, agg_mat = A, weights = fix_weights)

# Different weights for different forecast horizons
h_weights <- matrix(runif(4*3), 3, 4)
recoh <- cstd(base = topf, agg_mat = A, weights = h_weights)

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