3 citations · 3 across the 4 of their papers we have counts for
4 papers
Concurrency-Aware Process Model Forecasting with Causal Nets
Yongbo Yu, Jari Peeperkorn, Johannes De Smedt +1
Process model forecasting (PMF) aims to predict the process model that will characterize a future period, thereby providing a process-level view of how behavior is expected to evol…
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…
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models
Alexander Stevens, Johannes De Smedt
Although a recent shift has been made in the field of predictive process monitoring to use models from the explainable artificial intelligence field, the evaluation still occurs ma…