Corani, Giorgio and Azzimonti, Dario Filippo and Augusto, J.P.S.C. and Zaffalon, Marco (2020) Probabilistic reconciliation of hierarchical forecast via Bayes’ rule. In: Proceedings ECML - PKDD 2020.
Full text not available from this repository.Abstract
We present a novel approach for reconciling hierarchical forecasts, based on Bayes’ rule. We define a prior distribution for the bottom time series of the hierarchy, based on the bottom base forecasts. Then we update their distribution via Bayes’ rule, based on the base forecasts for the upper time series. Under the Gaussian assumption, we derive the updating in closed-form. We derive two algorithms, which differ as for the assumed independencies. We discuss their relation with the MinT reconciliation algorithm and with the Kalman filter, and we compare them experimentally.
Item Type: | Article in conference proceedings or Presentation at a conference (Paper) |
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Subjects: | Computer sciences |
Department/unit: | Dipartimento tecnologie innovative > Istituto Dalle Molle di studi sull’intelligenza artificiale USI-SUPSI |
Depositing User: | Giorgio Corani |
Date Deposited: | 18 Jun 2020 11:29 |
Last Modified: | 18 Jun 2020 11:29 |
URI: | http://repository.supsi.ch/id/eprint/11658 |
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