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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