7 papers
Dynamic Hypergraph Representation Learning for Multivariate Time Series without Prior Knowledge
Marco Gregnanin, Johannes De Smedt, Giorgio Gnecco +1
Hypergraphs have the capacity to capture higher-dimensional relationships among entities across various domains, making them a subject of growing interest within the research commu…
A Generative Adversarial Graph Neural Network for Synthetic Time Series Data
Marco Gregnanin, Johannes De Smedt, Giorgio Gnecco +1
Generating synthetic data for financial time series poses challenges, especially considering their non-stationary nature. Traditional statistical time series models normally assume…
The Statistical Significance of the Inclusion of Graph Neural Networks in the Financial Time Series Forecasting Problem
Marco Gregnanin, Johannes De Smedt, Giorgio Gnecco +1
Forecasting univariate time series in the financial market is a challenging endeavor. While numerous statistical and machine learning models have been introduced to address this ch…
Achieving Predictive Precision: Leveraging LSTM and Pseudo Labeling for Volvo's Discovery Challenge at ECML-PKDD 2024
Carlo Metta, Marco Gregnanin, Andrea Papini +5
This paper presents the second-place methodology in the Volvo Discovery Challenge at ECML-PKDD 2024, where we used Long Short-Term Memory networks and pseudo-labeling to predict ma…
A Systematization of the Wagner Framework: Graph Theory Conjectures and Reinforcement Learning
Flora Angileri, Giulia Lombardi, Andrea Fois +9
In 2021, Adam Zsolt Wagner proposed an approach to disprove conjectures in graph theory using Reinforcement Learning (RL). Wagner's idea can be framed as follows: consider a conjec…
GloNets: Globally Connected Neural Networks
Antonio Di Cecco, Carlo Metta, Marco Fantozzi +2
Deep learning architectures suffer from depth-related performance degradation, limiting the effective depth of neural networks. Approaches like ResNet are able to mitigate this, bu…
Signature-Based Community Detection for Time Series
Marco Gregnanin, Johannes De Smedt, Giorgio Gnecco +1
Community detection for time series without prior knowledge poses an open challenge within complex networks theory. Traditional approaches begin by assessing time series correlatio…