8 papers
fable.intermittent: benchmarking probabilistic forecasting methods for intermittent time series
Stefano Damato, Lorenzo Zambon, Giorgio Corani +1
Intermittent time series are common in spare-parts demand and retail sales. Since the cost of forecast errors is typically asymmetric, decisions such as inventory control require t…
End-to-end probabilistic hierarchical forecasting of large hierarchies via probabilistic top-down
Lorenzo Zambon, Dario Azzimonti, Giorgio Corani
Retail and supply chain operations rely on demand forecasts to drive decisions, from replenishment at the product level to capacity planning at the store level. These forecasts sho…
Model selection with proper scoring rules on data sets of time series: prefer the mean scaled score
Giorgio Corani, Stefano Damato, Dario Azzimonti +1
We study the problem of model selection among probabilistic forecasting models evaluated on datasets of multiple time series. The performance of a model on a single time series is…
Nonlinear Probabilistic Forecast Reconciliation
Anubhab Biswas, Lorenzo Zambon, Lorenzo Nespoli +1
Forecast reconciliation adjusts independently generated forecasts across multiple time series so that they satisfy some known constraints. While probabilistic forecast reconciliati…
Modeling uncertainty in the covariance matrix for probabilistic forecast reconciliation
Chiara Carrara, Dario Azzimonti, Giorgio Corani +1
In minimum trace (MinT) forecast reconciliation, the covariance matrix of the base forecast errors plays a crucial role. Typically, this matrix is estimated and then treated as kno…
Properties of the reconciled distributions for Gaussian and count forecasts
Lorenzo Zambon, Arianna Agosto, Paolo Giudici +1
Reconciliation enforces coherence between hierarchical forecasts, in order to satisfy a set of linear constraints. While most works focus on the reconciliation of the point forecas…
Efficient probabilistic reconciliation of forecasts for real-valued and count time series
Lorenzo Zambon, Dario Azzimonti, Giorgio Corani
Hierarchical time series are common in several applied fields. The forecasts for these time series are required to be coherent, that is, to satisfy the constraints given by the hie…
The Fourier Discrepancy Function
Auricchio Gennaro, Codegoni Andrea, Gualandi Stefano +1
In this paper, we propose the Fourier Discrepancy Function, a new discrepancy to compare discrete probability measures. We show that this discrepancy takes into account the geometr…