Stochastic Processes

The aleatory.processes module provides classes for the following stochastic processes

BESProcess([dim, initial, T, rng])	Bessel process
BESQProcess([dim, initial, T, rng])	Squared Bessel process
BrownianBridge([initial, end, T, rng])	Brownian Bridge
BrownianExcursion([T, rng])	Brownian Excursion
BrownianMeander([T, fixed_end, end, rng])	Brownian Meander
BrownianMotion([drift, scale, initial, T, rng])	Brownian Motion
CEVProcess(*args, **kwargs)	Constant Elasticity of Variance (CEV) process
CIRProcess([theta, mu, sigma, initial, T, rng])	Cox–Ingersoll–Ross (CIR) Process
CKLSProcess(*args, **kwargs)	Chan-Karolyi-Longstaff-Sanders (CKLS) process
fBM([hurst, T, rng])	Fractional Brownian motion
GBM([drift, volatility, initial, T, rng])	Geometric Brownian Motion
GaltonWatson([mu, rng])	Galton-Watson process
GammaProcess([mu, nu, T, rng])	Gamma process
GPConstant([sigma, T, rng])	GP with Constant Kernel
GPLinear([sigma, c, sigma_b, T, rng])	Gaussian Process with Linear Kernel
GPRBF([length_scale, sigma, T, rng])	Gaussian Process with Radial Basis Function (RBF) Kernel
GPSquaredExponential([length_scale, sigma, ...])	Gaussian Process with Squared Exponential Kernel
GPMatern([length_scale, sigma, nu, T, rng])	Gaussian Process with Matern Kernel
GPPeriodic([length_scale, sigma, period, T, rng])
GeneralRandomWalk([step_dist, step_args, ...])	General Random Walk
HawkesProcess([mu, alpha, beta, rng])	Hawkes process
InhomogeneousPoissonProcess(intensity[, rng])	Inhomogeneous Poisson Process
InverseGaussian([mu, scale, T, rng])	Inverse Gaussian process
MixedPoissonProcess(intensity[, ...])	Mixed Poisson Process
OUProcess([theta, sigma, initial, T, rng])	Ornstein–Uhlenbeck (OU) Process
PoissonProcess([rate, rng])	Poisson Process
RandomWalk([rng])	Simple Random Walk
VarianceGammaProcess([theta, nu, sigma, T, rng])	Variance Gamma Process
Vasicek([theta, mu, sigma, initial, T, rng])	Vasicek Process
WhiteNoise([sigma, T, rng])	Gaussian Process White Noise