3 citations · 3 across the 6 of their papers we have counts for
7 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…
Intermittent time series forecasting: local vs global models
Stefano Damato, Nicolò Rubattu, Dario Azzimonti +1
Forecasting intermittent time series, which contain zeros, is a crucial challenge in supply chains as inventory policies require probabilistic forecasts to establish safety levels.…
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…
Forecasting intermittent time series with Gaussian Processes and Tweedie likelihood
Stefano Damato, Dario Azzimonti, Giorgio Corani
We adopt Gaussian Processes (GPs) as latent functions for probabilistic forecasting of intermittent time series. The model is trained in a Bayesian framework that accounts for the…