hawkes-engine
Multivariate Hawkes processes with exponential kernels for Python: a compiled likelihood and gradient, a maximum-likelihood fit with standard errors, two independent exact simulators, time-rescaling diagnostics, and a lookup of what a pre-registered simulation study measured about the fit's standard errors in a given regime.
Status
Pre-alpha. Not on PyPI. Gates H0 to H8 of nine are complete; H9 is the first release. The wheel matrix is CPython 3.10 to 3.12 on Linux x86-64, macOS (x86-64 and arm64) and Windows AMD64, built by CI and not yet published.
A fit in a few lines
import numpy as np
import hawkes_engine as he
mu = np.array([0.6, 0.9])
alpha = np.array([[0.10, 0.35], [0.20, 0.05]]) # row = target, column = source
beta = np.array([[1.30, 1.70], [1.30, 1.70]])
times, types = he.simulate(mu, alpha, beta, T=20000.0, seed=1)
fit = he.fit(times, types, M=2, T=20000.0, seed=1)
print(fit)
print(fit.alpha.round(3).tolist(), fit.se["alpha"].round(3).tolist())
print(he.trust_lookup(N=len(times), rho_hat=fit.rho, beta=fit.beta.min(), T=20000.0).status)
Rows are targets and columns are sources: alpha[i, j] is the excitation a type-j
event applies to type i.
What was measured
Over 128 simulation cells with 2000 replications each, the standard errors the fit reports were smaller than the spread of its estimates: the ratio of mean reported standard error to empirical standard deviation is 0.779 +/- 0.011 pooled over the 12 cells of two types with at least 2000 events, and 0.61 for the decay block. The estimator is undefined where the observation window holds only a fraction of one decay time.
What is not established
- Two of the six pre-registered hypotheses, H6.3 and H6.4, were not confirmed. H6.6
was confirmed but is mostly mechanical: 96.4 percent of
unreliablefits have no standard error to begin with. FitResult.reliableis a conditioning report, not a statement about bias or coverage. It labelled fitsreliablewhose decay estimate was more than ten times the truth.- Nothing above one million events was timed, every timing is from one machine, and only two types (and a four-type satellite) were calibrated.
- No comparison with any other library has been made.
Each item, with its evidence, is on the Limits page.
Installation
Not on PyPI. Build from source (a C++20 compiler and CMake 3.24 are required) or install a wheel from the Actions tab:
git clone https://github.com/freddysccheung-067/hawkes-engine.git
cd hawkes-engine
python -m venv .venv
.venv/bin/python -m pip install -e ".[test]" # .venv\Scripts\ on Windows
.venv/bin/python examples/01_simulate_and_fit.py
docs/install.md has the commands for each operating system.
Where the evidence is
| Question | File |
|---|---|
| What the fit's standard errors were measured to do | studies/results/analysis/hypotheses.json, paper/draft.md |
| How the study was designed, before it ran | docs/preregistration.md at the tag H6-preregistration |
| What each gate established, and what is open | docs/STATE.md |
| How long a fit takes, and how much memory | benchmarks/results/capability_2026-10-21.md |
| The mathematics the code implements | docs/spec/mathematics.md |
| Why the reference implementation is frozen | docs/verification-rule.md |
Documentation: https://freddysccheung-067.github.io/hawkes-engine/. The
examples/ directory has five scripts that run in CI. See also
CONTRIBUTING.md, SECURITY.md and
CHANGELOG.md.
License
MIT: see LICENSE.
1 maintainer
Metadata
1 maintainer
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