cstd function

Cross-sectional top-down reconciliation

Cross-sectional top-down reconciliation

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

Arguments

  • base: A (h×1h \times 1) numeric vector containing the top-level base forecast; hh is the forecast horizon.
  • agg_mat: A (na×nbn_a \times n_b) numeric matrix representing the cross-sectional aggregation matrix. It maps the nbn_b bottom-level (free) variables into the nan_a upper (constrained) variables.
  • weights: A (h×nbh \times n_b) numeric matrix containing the proportions for the bottom time series; hh is the forecast horizon, and nbn_b is the total number of bottom variables.
  • normalize: If TRUE (default), the weights will sum to 1.

Returns

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

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)

References

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

See Also

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

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