29 citations · 110 across the 21 of their papers we have counts for
25 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…
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…
A tutorial on learning from preferences and choices with Gaussian Processes
Alessio Benavoli, Dario Azzimonti
Preference modelling lies at the intersection of economics, decision theory, machine learning and statistics. By understanding individuals' preferences and how they make choices, w…
Efficient Computation of Counterfactual Bounds
Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas +2
We assume to be given structural equations over discrete variables inducing a directed acyclic graph, namely, a structural causal model, together with data about its internal nodes…
Learning Choice Functions with Gaussian Processes
Alessio Benavoli, Dario Azzimonti, Dario Piga
In consumer theory, ranking available objects by means of preference relations yields the most common description of individual choices. However, preference-based models assume tha…
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…
Bounding Counterfactuals under Selection Bias
Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas +2
Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation. Previous work in this area focused on the deri…
Probabilistic Reconciliation of Count Time Series
Giorgio Corani, Dario Azzimonti, Nicolò Rubattu
Forecast reconciliation is an important research topic. Yet, there is currently neither formal framework nor practical method for the probabilistic reconciliation of count time ser…
Correlated Product of Experts for Sparse Gaussian Process Regression
Manuel Schürch, Dario Azzimonti, Alessio Benavoli +1
Gaussian processes (GPs) are an important tool in machine learning and statistics with applications ranging from social and natural science through engineering. They constitute a p…
Choice functions based multi-objective Bayesian optimisation
Alessio Benavoli, Dario Azzimonti, Dario Piga
In this work we introduce a new framework for multi-objective Bayesian optimisation where the multi-objective functions can only be accessed via choice judgements, such as ``I pick…
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes
Alessio Benavoli, Dario Azzimonti, Dario Piga
Skew-Gaussian processes (SkewGPs) extend the multivariate Unified Skew-Normal distributions over finite dimensional vectors to distribution over functions. SkewGPs are more general…
An exact kernel framework for spatio-temporal dynamics
Oleg Szehr, Dario Azzimonti, Laura Azzimonti
A kernel-based framework for spatio-temporal data analysis is introduced that applies in situations when the underlying system dynamics are governed by a dynamic equation. The key…
Preferential Bayesian optimisation with Skew Gaussian Processes
Alessio Benavoli, Dario Azzimonti, Dario Piga
Preferential Bayesian optimisation (PBO) deals with optimisation problems where the objective function can only be accessed via preference judgments, such as "this is better than t…
Orthogonally Decoupled Variational Fourier Features
Dario Azzimonti, Manuel Schürch, Alessio Benavoli +1
Sparse inducing points have long been a standard method to fit Gaussian processes to big data. In the last few years, spectral methods that exploit approximations of the covariance…
Skew Gaussian Processes for Classification
Alessio Benavoli, Dario Azzimonti, Dario Piga
Gaussian processes (GPs) are distributions over functions, which provide a Bayesian nonparametric approach to regression and classification. In spite of their success, GPs have lim…
Reconciling Hierarchical Forecasts via Bayes' Rule
Giorgio Corani, Dario Azzimonti, João P. S. C. Augusto +1
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 bo…
Recursive Estimation for Sparse Gaussian Process Regression
Manuel Schürch, Dario Azzimonti, Alessio Benavoli +1
Gaussian Processes (GPs) are powerful kernelized methods for non-parameteric regression used in many applications. However, their use is limited to a few thousand of training sampl…
Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding
Dario Azzimonti, David Ginsbourger, Jérémy Rohmer +1
We consider the problem of describing excursion sets of a real-valued function , i.e. the set of inputs where is above a fixed threshold. Such regions are hard to visualize…
Adaptive Design of Experiments for Conservative Estimation of Excursion Sets
Dario Azzimonti, David Ginsbourger, Clément Chevalier +2
We consider the problem of estimating the set of all inputs that leads a system to some particular behavior. The system is modeled by an expensive-to-evaluate function, such as a c…
Estimating orthant probabilities of high dimensional Gaussian vectors with an application to set estimation
Dario Azzimonti, David Ginsbourger
The computation of Gaussian orthant probabilities has been extensively studied for low-dimensional vectors. Here, we focus on the high-dimensional case and we present a two-step pr…