Uses a Game Theory approach to reconciliate hierarchical time series predicitons
1 2  gtop(preds_indiv, pred_total, weights_indiv, weight_total, bounds_indiv,
solver = "quad")

preds_indiv 
vector contains the individual predictions 
pred_total 
prediction for the sum of individuals 
weights_indiv 
vector, contains the weights of the individuals 
weight_total 
weight of the total 
bounds_indiv 
vector, contains the bounds of the individuals 
solver 
string, use quadratic programming ( 
In hierarchical time series forecasts, one predicts individuals quantities and a global quantity. There exists a contraint that matches the sum of the individual quantities to the global quantity. However, forecasting models don't take into account this constraint. With GTOP you can reconciliate the individual and global quantities in order to match the aggregate consistency contraint.
A list with
pred_indivs the reconciliated predictions for the individuals and the total,
solution the solution to the associate minimisation problem.
1 2 3 4 5 6 7 8 
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