A set of Python tools called STPPG for simulating any form of Marked Spatio-temporal self-exciting point processes.
There are two vital files included in this repo:
stppg.pybasic generators for homogeneous and inhomogeneous univariate point process, as well as different kinds of intensity classes and kernel functions.utils.pySome simple plotting functions for visualizing the point process simulation.
For simulating any parametric point process defined in references, we need to define the parametric form of its conditional density and generating space (time, location, and marks).
- To define the form of the conditional density of a point process, a
Lamobject defined instppg.pyhas to be substantiated with corresponding parameters. The most important parameters of a conditional intensityLamis defined in its kernel function. See the example below. - Regard the space of the point process, time space is specified by 1D two-elements list
T, and location & mark space is jointly specified by 2D listS, where first two sub-lists indicate the location X and Y, and the following sub-lists indicate the mark space.
A simple example for simulating a spatio-temporal hawkes point process equipped with a standard diffusion kernel by using stppg.
from stppg import StdDiffusionKernel, HawkesLam, MarkedSpatialTemporalPointProcess
from utils import plot_spatio_temporal_points, plot_spatial_intensity
np.random.seed(0)
np.set_printoptions(suppress=True)
# parameters initialization
mu = .1
kernel = StdDiffusionKernel(C=1., beta=1., sigma_x=.1, sigma_y=.1)
lam = HawkesLam(mu, kernel, maximum=1e+3)
pp = SpatialTemporalPointProcess(lam)
# generate points
points, sizes = pp.generate(
T=[0., 10.], S=[[-1., 1.], [-1., 1.]],
batch_size=500, verbose=True)
# plot intensity of the process over the time
plot_spatial_intensity(lam, points[0], S=[[0., 10.], [-1., 1.], [-1., 1.]],
t_slots=1000, grid_size=50, interval=50)And see the console output below.
[2018-09-21T08:28:59.974629-04:00] generate samples (10167, 3) from homogeneous poisson point process
[2018-09-21T08:28:59.975163-04:00] 0 raw samples have been checked. 0 samples have been retained.
[2018-09-21T08:29:00.097072-04:00] 1000 raw samples have been checked. 1 samples have been retained.
[2018-09-21T08:29:00.327207-04:00] 2000 raw samples have been checked. 11 samples have been retained.
[2018-09-21T08:29:00.643629-04:00] 3000 raw samples have been checked. 21 samples have been retained.
[2018-09-21T08:29:01.061958-04:00] 4000 raw samples have been checked. 51 samples have been retained.
[2018-09-21T08:29:01.592077-04:00] 5000 raw samples have been checked. 71 samples have been retained.
[2018-09-21T08:29:02.242894-04:00] 6000 raw samples have been checked. 88 samples have been retained.
[2018-09-21T08:29:02.974756-04:00] 7000 raw samples have been checked. 100 samples have been retained.
[2018-09-21T08:29:03.779483-04:00] 8000 raw samples have been checked. 110 samples have been retained.
[2018-09-21T08:29:04.741294-04:00] 9000 raw samples have been checked. 128 samples have been retained.
[2018-09-21T08:29:05.729931-04:00] 10000 raw samples have been checked. 146 samples have been retained.
[2018-09-21T08:29:05.904117-04:00] thining samples (147, 3) based on Spatio-temporal Hawkes point process intensity with mu=0, beta=1 and Diffusion-type Kernel.
[[0.06299644 0.9183208 0.97126597]
[0.11153312 0.280956 0.05509999]
[0.121267 0.36991751 0.65443569]
[0.12390124 0.49035887 0.64267112]
[0.13272256 0.9463777 0.02484997]
... ...
[0.97093684 0.13255896 0.15690197]
[0.97663217 0.6115613 0.34787382]
[0.97863127 0.21207182 0.19613095]
[0.98051828 0.43338621 0.67951877]
[0.99105454 0.21976152 0.26218951]]
[2018-09-21T08:29:05.911032-04:00] preparing the dataset 1000 × (50, 50) for plotting.
[2018-09-21T08:32:26.300910-04:00] start animation.The animations below show the progression of conditional intensities through time with different types of kernel functions.
| Standard Diffusion Kernel | Gaussian Diffusion Kernel | Gaussian Mixture Diffusion Kernel |
|---|---|---|
 |
 |
 |
- Shixiang Zhu, Yao Xie. "Reinforcement Learning of Spatio-Temporal Point Processes."
- Y. Ogata. "Space-Time Point-Process Models for Earthquake Occurrences"
- F. Musmeci, D. Vere-Jones. "A Space-Time Clustering Model for Historical Earthquakes"
- S. Zhu and Y. Xie. "Crime Linkage Detection by Spatio-Temporal-Textual Point Processes"
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