6 papers · 1 filter
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.…
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…
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…
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…