Title: | Exponential Multivariate Hawkes Model |
Version: | 0.9.8 |
Maintainer: | Kyungsub Lee <kyungsub@gmail.com> |
Description: | Simulate and fitting exponential multivariate Hawkes model. This package simulates a multivariate Hawkes model, introduced by Hawkes (1971) <doi:10.2307/2334319>, with an exponential kernel and fits the parameters from the data. Models with the constant parameters, as well as complex dependent structures, can also be simulated and estimated. The estimation is based on the maximum likelihood method, introduced by introduced by Ozaki (1979) <doi:10.1007/BF02480272>, with 'maxLik' package. |
Depends: | R (≥ 4.0.0) |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
Encoding: | UTF-8 |
RoxygenNote: | 7.3.2 |
Imports: | methods, maxLik |
Collate: | 'hspec.R' 'harrival.R' 'utilities.R' 'hmoment.R' 'hllf.R' 'hfit.R' 'hgfit.R' 'hreal.R' 'hsim.R' 'script.R' 'tzexp.R' 'zzz.R' |
Suggests: | knitr, rmarkdown, miscTools |
VignetteBuilder: | knitr |
URL: | https://github.com/ksublee/emhawkes, https://ksublee.github.io/emhawkes/ |
NeedsCompilation: | no |
Packaged: | 2025-08-26 02:33:51 UTC; Owner |
Author: | Kyungsub Lee [aut, cre] |
Repository: | CRAN |
Date/Publication: | 2025-08-26 04:20:11 UTC |
Expected Inter-Arrival Time
Description
Computes the conditional expected time until the next event.
Usage
expected_tau(
object,
rambda_component,
type = 1,
mu = NULL,
beta = NULL,
tol = .Machine$double.eps^0.25,
max_upper = Inf,
subdivisions = 400L
)
Arguments
object |
An object of class |
rambda_component |
Rambda component. |
type |
Process dimension index (default is 1). |
mu |
Optional mu value (overrides object@mu if provided). |
beta |
Optional beta value (overrides object@beta if provided). |
tol |
Relative tolerance for numerical integration. |
max_upper |
Upper integration limit. |
subdivisions |
Number of subdivisions for numerical integration. |
Value
Expected value of next inter-arrival time.
Perform Maximum Likelihood Estimation
Description
This is a generic function named hfit
designed for estimating the parameters
of the exponential Hawkes model. It is implemented as an S4 method for two main reasons:
Usage
hfit(
object,
inter_arrival = NULL,
type = NULL,
mark = NULL,
N = NULL,
Nc = NULL,
lambda_component0 = NULL,
N0 = NULL,
mylogLik = NULL,
reduced = TRUE,
grad = NULL,
hess = NULL,
constraint = NULL,
method = "BFGS",
verbose = FALSE,
...
)
## S4 method for signature 'hspec'
hfit(
object,
inter_arrival = NULL,
type = NULL,
mark = NULL,
N = NULL,
Nc = NULL,
lambda_component0 = NULL,
N0 = NULL,
mylogLik = NULL,
reduced = TRUE,
grad = NULL,
hess = NULL,
constraint = NULL,
method = "BFGS",
verbose = FALSE,
...
)
Arguments
object |
An |
inter_arrival |
A vector of inter-arrival times for events across all dimensions, starting with zero. |
type |
A vector indicating the dimensions, represented by numbers like 1, 2, 3, etc., starting with zero. |
mark |
A vector of mark (jump) sizes, starting with zero. |
N |
A matrix representing counting processes. |
Nc |
A matrix of counting processes weighted by mark sizes. |
lambda_component0 |
Initial values for the lambda component |
N0 |
Initial values for the counting processes matrix |
mylogLik |
A user-defined log-likelihood function, which must accept an |
reduced |
Logical; if |
grad |
A gradient matrix for the likelihood function. Refer to |
hess |
A Hessian matrix for the likelihood function. Refer to |
constraint |
Constraint matrices. Refer to |
method |
The optimization method to be used. Refer to |
verbose |
Logical; if |
... |
Additional parameters for optimization. Refer to |
Details
Model Representation: To represent the structure of the model as an hspec
object.
The multivariate marked Hawkes model has numerous variations, and using an S4 class
allows for a flexible and structured approach.
Optimization Initialization: To provide a starting point for numerical optimization.
The parameter values assigned to the hspec
slots serve as initial values for the optimization process.
This function utilizes the maxLik
package for optimization.
Value
maxLik
object
See Also
hspec-class
, hsim,hspec-method
Examples
# example 1
mu <- c(0.1, 0.1)
alpha <- matrix(c(0.2, 0.1, 0.1, 0.2), nrow=2, byrow=TRUE)
beta <- matrix(c(0.9, 0.9, 0.9, 0.9), nrow=2, byrow=TRUE)
h <- new("hspec", mu=mu, alpha=alpha, beta=beta)
res <- hsim(h, size=100)
summary(hfit(h, inter_arrival=res$inter_arrival, type=res$type))
# example 2
mu <- matrix(c(0.08, 0.08, 0.05, 0.05), nrow = 4)
alpha <- function(param = c(alpha11 = 0, alpha12 = 0.4, alpha33 = 0.5, alpha34 = 0.3)){
matrix(c(param["alpha11"], param["alpha12"], 0, 0,
param["alpha12"], param["alpha11"], 0, 0,
0, 0, param["alpha33"], param["alpha34"],
0, 0, param["alpha34"], param["alpha33"]), nrow = 4, byrow = TRUE)
}
beta <- matrix(c(rep(0.6, 8), rep(1.2, 8)), nrow = 4, byrow = TRUE)
impact <- function(param = c(alpha1n=0, alpha1w=0.2, alpha2n=0.001, alpha2w=0.1),
n=n, N=N, ...){
Psi <- matrix(c(0, 0, param['alpha1w'], param['alpha1n'],
0, 0, param['alpha1n'], param['alpha1w'],
param['alpha2w'], param['alpha2n'], 0, 0,
param['alpha2n'], param['alpha2w'], 0, 0), nrow=4, byrow=TRUE)
ind <- N[,"N1"][n] - N[,"N2"][n] > N[,"N3"][n] - N[,"N4"][n] + 0.5
km <- matrix(c(!ind, !ind, !ind, !ind,
ind, ind, ind, ind,
ind, ind, ind, ind,
!ind, !ind, !ind, !ind), nrow = 4, byrow = TRUE)
km * Psi
}
h <- new("hspec",
mu = mu, alpha = alpha, beta = beta, impact = impact)
hr <- hsim(h, size=100)
plot(hr$arrival, hr$N[,'N1'] - hr$N[,'N2'], type='s')
lines(hr$N[,'N3'] - hr$N[,'N4'], type='s', col='red')
fit <- hfit(h, hr$inter_arrival, hr$type)
summary(fit)
# example 3
mu <- c(0.15, 0.15)
alpha <- matrix(c(0.75, 0.6, 0.6, 0.75), nrow=2, byrow=TRUE)
beta <- matrix(c(2.6, 2.6, 2.6, 2.6), nrow=2, byrow=TRUE)
rmark <- function(param = c(p=0.65), ...){
rgeom(1, p=param[1]) + 1
}
impact <- function(param = c(eta1=0.2), alpha, n, mark, ...){
ma <- matrix(rep(mark[n]-1, 4), nrow = 2)
alpha * ma * matrix( rep(param["eta1"], 4), nrow=2)
}
h1 <- new("hspec", mu=mu, alpha=alpha, beta=beta,
rmark = rmark,
impact=impact)
res <- hsim(h1, size=100, lambda_component0 = matrix(rep(0.1,4), nrow=2))
fit <- hfit(h1,
inter_arrival = res$inter_arrival,
type = res$type,
mark = res$mark,
lambda_component0 = matrix(rep(0.1,4), nrow=2))
summary(fit)
# For more information, please see vignettes.
Realization of Hawkes Process
Description
hreal
is a list containing the following components:
-
hspec
: An S4 object of classhspec-class
that specifies the parameter values. -
inter_arrival
: The time intervals between consecutive events. -
arrival
: The cumulative sum ofinter_arrival
times. -
type
: An integer representing the type of event. -
mark
: The size of the mark, providing additional information associated with the event. -
N
: A counting process that tracks the number of events. -
Nc
: A counting process that tracks the number of events, weighted by mark. -
lambda
: The left-continuous intensity process. -
lambda_component
: The component of the intensity process,\lambda_{ij}
, that excludesmu
. -
rambda
: The right-continuous intensity process. -
rambda_component
: The right-continuous version oflambda_component
.
Functions for printing hreal
objects are provided.
Usage
## S3 method for class 'hreal'
print(x, n = 20, ...)
## S3 method for class 'hreal'
summary(object, n = 20, ...)
## S3 method for class 'hreal'
as.matrix(x, ...)
Arguments
x |
An S3 object of class |
n |
The number of rows to display. |
... |
Additional arguments passed to or from other methods. |
object |
An S3 object of class |
Simulate multivariate Hawkes process with exponential kernel.
Description
The method simulate multivariate Hawkes processes.
The object hspec-class
contains the parameter values such as mu
, alpha
, beta
.
The mark (jump) structure may or may not be included.
It returns an object of class hreal
which contains inter_arrival
, arrival
,
type
, mark
, N
, Nc
, lambda
, lambda_component
, rambda
, rambda_component
.
Usage
hsim(
object,
size = 100,
lambda_component0 = NULL,
N0 = NULL,
Nc0 = NULL,
verbose = FALSE,
...
)
## S4 method for signature 'hspec'
hsim(
object,
size = 100,
lambda_component0 = NULL,
N0 = NULL,
Nc0 = NULL,
verbose = FALSE,
...
)
Arguments
object |
|
size |
Number of observations. |
lambda_component0 |
Initial values for the lambda component |
N0 |
Starting values of N with default value 0. |
Nc0 |
Starting values of Nc with default value 0. |
verbose |
Logical. If |
... |
Further arguments passed to or from other methods. |
Value
hreal
S3-object, summary of the Hawkes process realization.
Examples
# example 1
mu <- 1; alpha <- 1; beta <- 2
h <- new("hspec", mu=mu, alpha=alpha, beta=beta)
hsim(h, size=100)
# example 2
mu <- matrix(c(0.1, 0.1), nrow=2)
alpha <- matrix(c(0.2, 0.1, 0.1, 0.2), nrow=2, byrow=TRUE)
beta <- matrix(c(0.9, 0.9, 0.9, 0.9), nrow=2, byrow=TRUE)
h <- new("hspec", mu=mu, alpha=alpha, beta=beta)
res <- hsim(h, size=100)
print(res)
An S4 Class Representing an Exponential Marked Hawkes Model
Description
This class defines a marked Hawkes model with an exponential kernel. The intensity of the ground process is expressed as:
\lambda(t) = \mu + \int_{(-\infty,t)\times E} ( \alpha + g(u, z) ) e^{-\beta (t-u)} M(du \times dz).
For more details, refer to the vignettes.
Details
\mu
is base intensity, typically a constant vector or a function.
\alpha
is a constant matrix representing the impact on intensities after events, stored in the alpha
slot.
\beta
is a constant matrix for exponential decay rates, stored in the beta
slot.
z
represents the mark and can be generated by rmark
slot.
g
is represented by eta
when it is linear function of z
, and by impact
when it is a genenral function.
mu
, alpha
and beta
are required slots for every exponential Hawkes model.
rmark
and impact
are additional slots.
Slots
mu
A numeric value, matrix, or function. If numeric, it is automatically converted to a matrix.
alpha
A numeric value, matrix, or function. If numeric, it is automatically converted to a matrix, representing the exciting term.
beta
A numeric value, matrix, or function. If numeric, it is automatically converted to a matrix, representing the exponential decay.
eta
A numeric value, matrix, or function. If numeric, it is automatically converted to a matrix, representing the impact of an additional mark.
impact
A function describing the after-effects of the mark on
\lambda
, with the first argument always beingparam
.dimens
The dimension of the model.
rmark
A function that generates marks for the counting process, used in simulations.
dmark
A density function for the mark, used in estimation.
type_col_map
A mapping between type and column number of the kernel used in multi-kernel models.
rresidual
A function for generating residuals, analogous to the R random number generator function, specifically for the discrete Hawkes model.
dresidual
A density function for the residual.
presidual
A distribution function for the residual.
qresidual
A quantile function for the residual.
Examples
MU <- matrix(c(0.2), nrow = 2)
ALPHA <- matrix(c(0.75, 0.92, 0.92, 0.75), nrow = 2, byrow=TRUE)
BETA <- matrix(c(2.25, 2.25, 2.25, 2.25), nrow = 2, byrow=TRUE)
mhspec2 <- new("hspec", mu=MU, alpha=ALPHA, beta=BETA)
mhspec2
Compute Hawkes volatility
Description
This function computes Hawkes volatility. Only works for bi-variate Hawkes process.
Usage
hvol(
object,
horizon = 1,
inter_arrival = NULL,
type = NULL,
mark = NULL,
dependence = FALSE,
lambda_component0 = NULL,
...
)
## S4 method for signature 'hspec'
hvol(
object,
horizon = 1,
inter_arrival = NULL,
type = NULL,
mark = NULL,
dependence = FALSE,
lambda_component0 = NULL,
...
)
Arguments
object |
|
horizon |
Time horizon for volatility. |
inter_arrival |
Inter-arrival times of events which includes inter-arrival for events that occur in all dimensions. Start with zero. |
type |
A vector of dimensions. Distinguished by numbers, 1, 2, 3, and so on. Start with zero. |
mark |
A vector of mark (jump) sizes. Start with zero. |
dependence |
Dependence between mark and previous sigma-algebra. |
lambda_component0 |
A matrix of the starting values of lambda component. |
... |
Further arguments passed to or from other methods. |
Infer lambda process with given Hawkes model and realized path
Description
This method compute the inferred lambda process and returns it as hreal
form.
If we have realized path of Hawkes process and its parameter value, then we can compute the inferred lambda processes.
Similarly with other method such as hfit
, the input arguments are inter_arrival
, type
, mark
,
or equivalently, N
and Nc
.
Usage
infer_lambda(
object,
inter_arrival = NULL,
type = NULL,
mark = NULL,
N = NULL,
Nc = NULL,
lambda_component0 = NULL,
N0 = NULL,
Nc0 = NULL,
...
)
## S4 method for signature 'hspec'
infer_lambda(
object,
inter_arrival = NULL,
type = NULL,
mark = NULL,
N = NULL,
Nc = NULL,
lambda_component0 = NULL,
N0 = NULL,
Nc0 = NULL,
...
)
Arguments
object |
|
inter_arrival |
inter-arrival times of events. This includes inter-arrival for events that occur in all dimensions. Start with zero. |
type |
a vector of dimensions. Distinguished by numbers, 1, 2, 3, and so on. Start with zero. |
mark |
a vector of mark (jump) sizes. Start with zero. |
N |
Hawkes process. If not provided, then generate using inter_arrival and type. |
Nc |
mark accumulated Hawkes process. If not provided, then generate using inter_arrival, type and mark. |
lambda_component0 |
Initial values for the lambda component |
N0 |
the initial values of N. |
Nc0 |
the initial values of Nc. |
... |
further arguments passed to or from other methods. |
Value
hreal
S3-object, with inferred intensity.
Examples
mu <- c(0.1, 0.1)
alpha <- matrix(c(0.2, 0.1, 0.1, 0.2), nrow=2, byrow=TRUE)
beta <- matrix(c(0.9, 0.9, 0.9, 0.9), nrow=2, byrow=TRUE)
h <- new("hspec", mu=mu, alpha=alpha, beta=beta)
res <- hsim(h, size=100)
summary(res)
res2 <- infer_lambda(h, res$inter_arrival, res$type)
summary(res2)
Compute the Log-Likelihood Function
Description
Calculates the log-likelihood for the Hawkes model.
Usage
## S4 method for signature 'hspec'
logLik(
object,
inter_arrival,
type = NULL,
mark = NULL,
N = NULL,
Nc = NULL,
N0 = NULL,
Nc0 = NULL,
lambda_component0 = NULL,
infer = FALSE,
...
)
Arguments
object |
An |
inter_arrival |
A vector of inter-arrival times for events across all dimensions, starting with zero. |
type |
A vector indicating the dimensions, represented by numbers (1, 2, 3, etc.), starting with zero. |
mark |
A vector of mark (jump) sizes, starting with zero. |
N |
A matrix representing counting processes. |
Nc |
A matrix of counting processes weighted by mark sizes. |
N0 |
A matrix of initial values for |
Nc0 |
A matrix of initial values for |
lambda_component0 |
Initial values for the lambda component |
infer |
Logical |
... |
Additional arguments passed to or from other methods. |
See Also
hspec-class
, hfit,hspec-method
Compute residual process
Description
Using random time change, this function compute the residual process, which is the inter-arrival time of a standard Poisson process. Therefore, the return values should follow the exponential distribution with rate 1, if model and rambda are correctly specified.
Usage
residual_process(
component,
inter_arrival,
type,
rambda_component,
mu,
beta,
dimens = NULL,
mark = NULL,
N = NULL,
Nc = NULL,
lambda_component0 = NULL,
N0 = NULL,
...
)
Arguments
component |
The component of type to get the residual process. |
inter_arrival |
Inter-arrival times of events. This includes inter-arrival for events that occur in all dimensions. Start with zero. |
type |
A vector of types distinguished by numbers, 1, 2, 3, and so on. Start with zero. |
rambda_component |
Right continuous version of lambda process. |
mu |
Numeric value or matrix or function. If numeric, automatically converted to matrix. |
beta |
Numeric value or matrix or function. If numeric, automatically converted to matrix, exponential decay. |
dimens |
Dimension of the model. If omitted, set to be the length of |
mark |
A vector of realized mark (jump) sizes. Start with zero. |
N |
A matrix of counting processes. |
Nc |
A matrix of counting processes weighted by mark. |
lambda_component0 |
The initial values of lambda component. Must have the same dimensional matrix with |
N0 |
The initial value of N |
... |
Further arguments passed to or from other methods. |
Examples
mu <- c(0.1, 0.1)
alpha <- matrix(c(0.2, 0.1, 0.1, 0.2), nrow=2, byrow=TRUE)
beta <- matrix(c(0.9, 0.9, 0.9, 0.9), nrow=2, byrow=TRUE)
h <- new("hspec", mu=mu, alpha=alpha, beta=beta)
res <- hsim(h, size=1000)
rp <- residual_process(component = 1, res$inter_arrival, res$type, res$rambda_component, mu, beta)
Set Residual Distribution Functions for Hawkes Model Specification
Description
Sets residual distribution functions (density, CDF, quantile, and random generation) for a Hawkes model specification object with fixed parameters.
Usage
set_residual(
object,
param,
dresidual = NULL,
presidual = NULL,
qresidual = NULL,
rresidual = NULL,
...
)
## S4 method for signature 'hspec'
set_residual(
object,
param,
dresidual = NULL,
presidual = NULL,
qresidual = NULL,
rresidual = NULL
)
Arguments
object |
An object of class |
param |
A named numeric vector of parameters for the residual distribution |
dresidual |
Density function of the residual distribution (optional) |
presidual |
Cumulative distribution function (CDF) of the residual distribution (optional) |
qresidual |
Quantile function of the residual distribution (optional) |
rresidual |
Random generation function of the residual distribution (optional) |
... |
Additional arguments for future extensions |
Details
This method allows setting residual distribution functions for a flexible model.
The param
argument in these functions defaults to the parameters provided during
setup and is used for estimation.
Value
An updated hspec
object with residual functions set
Examples
## Not run:
# Create basic Hawkes specification
hspec_obj <- new("hspec",
mu = matrix(0.1, nrow = 1),
alpha = matrix(0.5, nrow = 1),
beta = matrix(1.0, nrow = 1))
# Set residual distribution parameters
params <- c(a = 0.5, ell = 1.0)
# Apply residual functions
hspec_obj <- set_residual(
hspec_obj,
param = params,
dresidual = dtzexp,
presidual = ptzexp,
qresidual = qtzexp,
rresidual = rtzexp
)
# Check resulting functions
hspec_obj@dresidual
hspec_obj@rresidual
## End(Not run)
Trapezoid + Exponential Distribution
Description
These functions implement a custom distribution combining a trapezoidal section (0 < x < a)
and an exponential tail (x \geq a
). The distribution is parameterized by:
-
a
: transition point between trapezoid and exponential -
ell
: rate parameter for the exponential tail
Usage
dtzexp(x, a, ell)
ptzexp(q, a, ell)
qtzexp(p, a, ell)
rtzexp(n, a, ell)
Arguments
x , q |
vector of quantiles |
a |
location parameter for transition (must be > 0) |
ell |
rate parameter for exponential decay (must be > 0) |
p |
vector of probabilities |
n |
number of observations |
Details
Density, distribution function, quantile function and random generation for a custom trapezoid + exponential distribution.
The trapezoid+exponential distribution has the probability density function:
f(x) = \begin{cases}
0 & \text{if } x \leq 0 \\
\frac{(p\ell - c)}{a} x + c & \text{if } 0 < x < a \\
p\ell e^{-\ell (x - a)} & \text{if } x \geq a
\end{cases}
where:
p = \frac{\ell - \frac{a\ell}{3}}{\frac{a^2\ell^2}{6} + \frac{2a\ell}{3} + 1}
c = \frac{2 - 2p - p\ell a}{a}
The trapezoid+exponential distribution has the following characteristics:
Support on
[0, \infty)
Continuous probability distribution
Linear density from 0 to a
Exponential decay for x > a
Value
-
dtzexp
gives the density -
ptzexp
gives the distribution function -
qtzexp
gives the quantile function -
rtzexp
generates random deviates