15 papers
Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring
Kseniya Sahatova, Rafael Seidi Oyamada, Xuefei Lu +1
Predictive process monitoring supports the optimization and control of operational business processes by forecasting the future state or outcome of ongoing cases. While deep neural…
DIFF-ERO: A Conformance-Aware Loss for Deep Learning in Process Mining
Johannes De Smedt, Jari Peeperkorn, Artem Polyvyanyy +1
Deep learning has driven many recent advances in process analytics, especially for predictive and prescriptive monitoring. However, standard objectives such as cross-entropy optimi…
SCOPE: Sequential Causal Optimization of Process Interventions
Jakob De Moor, Hans Weytjens, Johannes De Smedt +1
Prescriptive Process Monitoring (PresPM) recommends interventions during running business processes to optimize key performance indicators (KPIs). In realistic settings, interventi…
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…