Learnability of Timescale Graphical Event Models
This work addresses a gap in the literature for researchers in graphical event models, but it appears incremental as it refines existing methods.
The authors tackled the problem of determining hyperparameters and refining distance measures in Timescale Graphical Event Models, proposing and evaluating different heuristics through a benchmark on synthetic data to assess their applicability.
This technical report tries to fill a gap in current literature on Timescale Graphical Event Models. I propose and evaluate different heuristics to determine hyper-parameters during the structure learning algorithm and refine an existing distance measure. A comprehensive benchmark on synthetic data will be conducted allowing conclusions about the applicability of the different heuristics.