Examples¶

How to use tick in practise?

../_images/plot_hawkes_finance_data.png

Fit Hawkes on finance data¶

../_images/plot_simulation_coxreg.png

Cox regression data simulation¶

../_images/plot_logistic_adult.png

Binary classification with logistic regression¶

../_images/plot_poisson_inhomogeneous.png

Inhomogeneous Poisson process simulation¶

../_images/plot_hawkes_adm4.png

Fit sparse and low rank adjacency matrix with Hawkes ADM4¶

../_images/plot_poisson_regression.png

Comparison of solvers for Poisson regression¶

../_images/plot_hawkes_gaussian_kernels.png

Fit Hawkes with asynchronous causality¶

../_images/plot_hawkes_time_func_simu.png

Hawkes simulation with exotic kernels¶

../_images/plot_hawkes_varying_baseline.png

Hawkes process with non constant baseline¶

../_images/plot_hawkes_conditional_law.png

Fit Hawkes power law kernels¶

../_images/plot_hawkes_varying_baseline_simulation.png

Simulate Hawkes process with non constant baseline¶

../_images/plot_hawkes_em.png

Fit Hawkes random kernels¶

../_images/plot_prox_example.png

Examples of proximal operators¶

../_images/plot_low_precision_learner.png

Precision vs speed tradeoff¶

../_images/plot_simulation_linear_model.png

Linear models data simulation¶

../_images/plot_hawkes_cumulants_matching.png

Fit Hawkes kernel norms using cumulants¶

../_images/plot_hawkes_estimated_intensity.png

Plot estimated intensity of Hawkes processes¶

../_images/plot_svrg_with_auto_step.png

Automatic step choice¶

../_images/plot_hawkes_basis_kernels.png

Fit exotic Hawkes kernels¶

../_images/plot_robust_linear_regression.png

Robust linear model example¶

../_images/plot_asynchronous_stochastic_solver.png

Asynchronous stochastic solver¶

../_images/plot_logistic_tick_vs_scikit.png

Logistic regression comparison: scikit-learn versus tick¶

../_images/plot_2d_linear_regression.png

Linear regression in two dimensions¶

../_images/plot_glm_convergence.png

Generalized linear models solver convergence¶

../_images/plot_conv_sccs_cv_results.png

ConvSCCS cross validation on simulated longitudinal features example¶