4 citations · 6 across the 2 of their papers we have counts for
3 papers
Online Continual Learning for Time Series: a Natural Score-driven Approach
Edoardo Urettini, Daniele Atzeni, Ioanna-Yvonni Tsaknaki +1
Online continual learning (OCL) methods adapt to changing environments without forgetting past knowledge. Similarly, online time series forecasting (OTSF) is a real-world problem w…
GAS-Norm: Score-Driven Adaptive Normalization for Non-Stationary Time Series Forecasting in Deep Learning
Edoardo Urettini, Daniele Atzeni, Reshawn J. Ramjattan +1
Despite their popularity, deep neural networks (DNNs) applied to time series forecasting often fail to beat simpler statistical models. One of the main causes of this suboptimal pe…
Modeling Edge Features with Deep Bayesian Graph Networks
Daniele Atzeni, Federico Errica, Davide Bacciu +1
We propose an extension of the Contextual Graph Markov Model, a deep and probabilistic machine learning model for graphs, to model the distribution of edge features. Our approach i…