Probabilistic reconciliation of count time series

Giorgio Corani, Dario Azzimonti and Nicolò Rubattu

International Journal of Forecasting, 2024, vol. 40, issue 2, 457-469

Abstract: Forecast reconciliation is an important research topic. Yet, there is currently neither a formal framework nor a practical method for the probabilistic reconciliation of count time series. This paper proposes a definition of coherency and reconciled probabilistic forecast, which applies to real-valued and count variables, and a novel method for probabilistic reconciliation. It is based on a generalization of Bayes’ rule and can reconcile 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.

Keywords: Forecast reconciliation; Probabilistic reconciliation; Count time series; Virtual evidence; Bayes rule (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:40:y:2024:i:2:p:457-469

DOI: 10.1016/j.ijforecast.2023.04.003

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