The middle-out forecast reconciliation (Athanasopoulos et al., 2009) combines top-down (cstd) and bottom-up (csbu) for genuine hierarchical/grouped time series. Given the base forecasts of variables at an intermediate level \(l\), it performs

a top-down approach for the levels \(<l\);

a bottom-up approach for the levels \(>l\).

## Arguments

- base
A (\(h \times n_l\)) numeric matrix containing the \(l\)-level base forecast; \(n_l\) is the number of variables at level \(l\), and \(h\) 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.

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

`agg_mat`

.- 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.

## References

Athanasopoulos, G., Ahmed, R. A. and Hyndman, R.J. (2009) Hierarchical forecasts
for Australian domestic tourism. *International Journal of Forecasting* 25(1),
146–166. doi:10.1016/j.ijforecast.2008.07.004

## 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 2) top base forecasts vector (simulated), forecast horizon = 3
baseL2 <- matrix(rnorm(2*3, 5), 3, 2)
# Same weights for different forecast horizons
fix_weights <- runif(4)
reco <- csmo(base = baseL2, agg_mat = A, id_rows = 2:3, weights = fix_weights)
# Different weights for different forecast horizons
h_weights <- matrix(runif(4*3), 3, 4)
recoh <- csmo(base = baseL2, agg_mat = A, id_rows = 2:3, weights = h_weights)
```