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Learning Granger Causality for Hawkes Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add sparse-group-lasso regularizer to MLE (MLE-SGL vs pure MLE) (ablation) | Hawkes process impact functions on IPTV data · IPTV viewing data, Loglike | Loglike (x10^6): -1.904 to -1.880 at 1 month; -1.876 to -1.872 at 10 months | Helped |
| replace ODE-based estimator with MLE-SGL | Hawkes process on IPTV data · IPTV viewing data, Loglike | Loglike (x10^6): -2.066 to -1.880 at 1 month | Helped |
| add group-lasso regularizer to MLE (ablation) | Hawkes process on synthetic data · synthetic sine-like and piecewise data | e_mu, e_phi, Loglike | Helped |
| add pairwise similarity (clustering) constraint to MLE-SGL (ablation) | Hawkes process on synthetic data · synthetic data with clustering structure | Loglike, e_mu, e_phi | Helped |
| add sparse-group-lasso to pure MLE for recovering all-zero impact functions (ablation) | Hawkes process on synthetic data · synthetic data, 500 training sequences | recovery of all-zero impact functions (Granger graph) | Helped |
| use Gaussian basis functions with mismatched piecewise-constant impact functions | MLE-SGLP on synthetic piecewise data · synthetic piecewise constant data | Granger graph recovery | No clear effect |
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