Probabilistic Reconciliation of Count Time Series
- URL: http://arxiv.org/abs/2207.09322v4
- Date: Wed, 26 Apr 2023 15:03:39 GMT
- Title: Probabilistic Reconciliation of Count Time Series
- Authors: Giorgio Corani, Dario Azzimonti, Nicol\`o Rubattu
- Abstract summary: We propose a definition of coherency and reconciled probabilistic forecast.
It applies to both real-valued and count variables.
It is based on a generalization of Bayes' rule and it can reconcile both real-value and count variables.
- Score: 0.6810856082577402
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Forecast reconciliation is an important research topic. Yet, there is
currently neither formal framework nor practical method for the probabilistic
reconciliation of count time series. In this paper we propose a definition of
coherency and reconciled probabilistic forecast which applies to both
real-valued and count variables and a novel method for probabilistic
reconciliation. It is based on a generalization of Bayes' rule and it can
reconcile both real-value and count variables. When applied to count variables,
it yields a reconciled probability mass function. Our experiments with the
temporal reconciliation of count variables show a major forecast improvement
compared to the probabilistic Gaussian reconciliation.
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