hawkesbow

Felix Cheysson

This package implements a spectral approach to the parametric estimation of Hawkes processes from Binned Observations through Whittle likelihood (HawkesBOW). It is based on the results of the article (Cheysson and Lang 2020).

Installation

You can install the released version of hawkesbow from GitHub with:

devtools::install_github("fcheysson/hawkesbow")

The Hawkes process

Hawkes processes form a family of models for point processes for which the occurrence of an event temporarily increases the probability of future events occurring (Hawkes 1971). Formally, a Hawkes process on can be defined from its conditional intensity : where the variables denote the arrival times of the process, the immigration intensity is a positive constant, and the reproduction function is a measurable function. The reproduction function can be further decomposed as , where is called the and is a true density function, , called the .

Alternatively, the Hawkes process can be constructed as a poissonian cluster process (Hawkes and Oakes 1974). The process consists of a flow of immigrants, the cluster centres, arriving according to a homogeneous Poisson process of intensity . Then, an immigrant arriving at time generates children according to an inhomogeneous Poisson process of intensity . These in turn independently generate children according to the same process, and so on ad infinitum. The processes consisting of an immigrant and all its descendants are therefore branching processes, and are independent of each other. Finally, the process resulting from the superposition of all these branching processes is a Hawkes process of conditional intensity (see the following figure of a realisation of an exponential Hawkes process with , ).

This package also supports non-causal Hawkes processes, for which the reproduction kernel may take non-negative values on . Such processes are not defined through their conditional intensity functions, but through the poisson cluster representation, where each individual can potentially generate offsprings both in the future and in the past.

The count sequence of a Hawkes process is the time series generated by the event counts of the process, that is the series obtained by counting the events of the process on intervals of fixed length. Formally, the count sequence with bin size associated to the point process is the sequence .

Estimation procedure

This package fully supports the parametric estimation of Hawkes processes from their count sequences via minimisation of the Whittle likelihood, and partially supports the estimation from the arrival times via maximisation of the likelihood.

Maximum likelihood estimation

The parameters of a Hawkes process with arrival times on the interval can be estimated by maximising the following pseudo-likelihood

Note that each step of the optimisation is usually of complexity .

Minimisation of the Whittle likelihood

Alternatively, the parameters of a Hawkes process with count sequence can be estimated by minimising the log-spectral (Whittle) likelihood

where denotes the periodogram of and its spectral density function. Note that each step of the optimisation is of complexity and the periodogram can be calculated in complexity using a Fast Fourier Transform algorithm, making this method usually faster than maximum likelihood estimation.

Main usage

This package supports both Hawkes processes and their count sequences.

Simulation of Hawkes processes

Hawkes processes can be simulated by the function hawkes:

hawkes(end, fun, repr, family, M=null, ...)

where end denotes the end of the interval . This function uses the cluster representation:

• First, the immigrants are drawn according to a (potentially inhomogeneous) Poisson process with intensity measure fun.

• Second, the number of offsprings of an immigrant are drawn from a Poisson distribution with intensity repr.

• Third, these offsprings are distributed according to the family distribution.

• Then, further offsprings are generated according to the last two steps.

The argument fun can take a numeric value or be specified as a function, in which case the argument M must be specified as an upper bound on fun (to allow simulation of the immigration process by thinning). The argument family can either be specified as a string name corresponding to a distribution with random generation function rname (for example exp for rexp), or directly as a random generation function. The optional arguments ... are passed to the random generation function specified by family.

This returns a list of class hawkes, whose realisations are stored in the member p. Other members of the list are mainly used for support functions.

Examples

• Simulate a Hawkes process with immigration intensity , reproduction mean and reproduction kernel on :

x <- hawkes(10, fun = 1, repr = 0.5, family = "exp", rate = 2)

• Simulate a Hawkes process with inhomogeneous immigration process with intensity , reproduction mean and -triangular reproduction kernel :

x <- hawkes(10, fun=function(y) {1+sin(y)}, M=2, repr=0.25,
            family=function(n) {1 - sqrt(1 - runif(n))})

Plot function

• Hawkes processes can be plotted with the function plot.hawkes:

plot.hawkes(x, intensity = FALSE, precision = 1e3, fun = NULL, repr = NULL, family = NULL, 
            M = NULL, ...)

If x is of class hawkes, as for objects returned by the function hawkes, arguments fun through M can be ignored. If intensity is set to FALSE, this plots the genealogy of the simulated Hawkes process (as in preceding figure, bottom panel). If it is set to TRUE, this plots the conditional intensity of the process (as in preceding figure, top panel).

Estimation of Hawkes processes

Two functions implement the estimation of Hawkes processes: mle from arrival times and whittle from count sequences . While the optimisation procedure rely on existing functions (see below), calculations of both the usual and Whittle likelihood functions are done in C++ via Rcpp (Eddelbuettel and François 2011) and RcppArmadillo (Eddelbuettel and Sanderson 2014).

By maximum likelihood

The maximum likelihood method is implemented by the function

mle(events, kern, end, init = NULL, opts = NULL, ...)

events holds the arrival times in ascending order, kern must be a string (partially) matching one of the reproduction kernels (see below) and end denotes the endpoint of observation of the process.

The optimisation of the maximum likelihood function is done by the function nloptr from the package nloptr (Johnson, n.d.) with algorithm L-BFGS-B, where the derivatives of the likelihood are calculated explicitly. By default, parameters are constrained to be positive, and additionally is constrained to be below 1. However, both the arguments opts and ... are passed on to nloptr, so the algorithm, the constraints, or any other parameter of the optimisation procedure can be changed.

Example of use:

x = hawkes(100, fun = 1, repr = .5, family = "exp", rate = 1)
mle(x$p, "Exponential", x$end)

By minimisation of the Whittle likelihood

The Whittle likelihood method is implemented by the function

whittle(counts, kern, binsize = NULL, trunc = 5L, init = NULL, ...)

counts holds the count sequence , kern must be a string (partially) matching one of the reproduction kernels (see below), binsize denotes the bin size and trunc is the number of foldings due to aliasing taken into account.

The optimisation of the Whittle likelihood function is done by the function optim, with algorithm L-BFGS-B where the derivatives of the likelihood are approximated by finite differences. By default, parameters are constrained to be positive, and additionally is constrained to be below 1. However, the argument ... is passed to optim, so any optimisation parameter can be changed.

Example of use:

x = hawkes(1000, fun = 1, repr = .5, family = "exp", rate = 1)
y = discrete(x, binsize = 1)
whittle(y, "Exponential", 1)

Note that discrete is a useful function to create the count sequence associated with an object x of class hawkes.

Reproduction kernels

We introduce the reproduction kernels that are currently implemented in this package. Recall that the Fourier transform of a reproduction kernel is given by

and that it is a Hermitian function .

The exponential kernel

This is the exponential density function with parameter :

Its Fourier transform is

The exponential kernel can be specified with the string Exponential and the parameter with the usual argument rate. Both maximum and Whittle likelihood methods are fully implemented for exponential kernels. Moreover, the likelihood function is implemented in complexity , using the relations in (Ozaki and Ogata 1979).

The symmetric exponential kernel

This is a symmetrised version of the exponential density function with parameter :

Its Fourier transform is

The symmetric exponential kernel can be specified with the string SymmetricExponential and the parameter with the argument rate. Only the Whittle likelihood method is implemented for symmetric exponential kernels. Note that it is a non-causal kernel, as for .

The gaussian kernel

This is the gaussian density function with mean and variance :

Its Fourier transform is

The gaussian kernel can be specified with the string Gaussian and its parameters and with the usual arguments mean and sd respectively. Only the Whittle likelihood method is implemented for gaussian kernels. Note that it is a non-causal kernel, as for .

The power law kernel

This is a normalised and shifted power law function, with shape and scale :

For positive , its Fourier transform is given by

where denotes the integral

With successive integration by parts, this integral can be related to , with .

If or equivalently , the integral , called the exponential integral with imaginary argument, can be related the trigonometric integrals and calculated using Padé approximants (Rowe et al. 2015, Appendix B), accurate to better than . The function E1_imaginary implements this approximation.

If or equivalently , the integral can be related to the incomplete gamma function with imaginary argument

where . We implemented Taylor approximations of this integral, accurate to better than , in the function inc_gamma_imag.

The power law kernel can be specified with the string PowerLaw and its parameters and with the arguments shape and scale respectively. Both maximum and Whittle likelihood methods are implemented for power law kernels.

The Pareto kernels

This is the Pareto density function with shape and scale :

For positive , its Fourier transform is given by

see above for the definition of .

Only Pareto kernels with fixed , , and have been implemented and can specified with the strings Pareto1, Pareto2 and Pareto3 respectively, with parameter specified with the argument scale. Only the Whittle method is available for Pareto kernels.

To be implemented

• Improve this vignette: it is currently too sparse and functions of the package could need some better description.

• Add some real datasets to the package: real life case-studies with good datasets help understand the functionalities of a package.

• Variance and confidence intervals for the estimation with function whittle: note that currently, the variance-covariance matrix returned by the optimisation method in function whittle is not accurate, as it ignores the dependence within the count sequence .

• Diagnostics for the estimated model: spectral density based goodness-of-fit tests are to be implemented for the estimated Hawkes processes, based on the work of (Paparoditis 2000).

• Custom built-kernels: allow the user to input reproduction kernels that are not already implemented.

References

Cheysson, Felix, and Gabriel Lang. 2020. “Strong mixing condition for Hawkes processes and application to Whittle estimation from count data.” arXiv, March. https://arxiv.org/abs/2003.04314.

Eddelbuettel, Dirk, and Romain François. 2011. “Rcpp : Seamless R and C++ Integration.” J. Stat. Softw. 40 (8): 1–18. https://doi.org/10.18637/jss.v040.i08.

Eddelbuettel, Dirk, and Conrad Sanderson. 2014. “RcppArmadillo: Accelerating R with High-Performance C++ Linear Algebra.” Computational Statistics and Data Analysis 71: 1054–63. http://dx.doi.org/10.1016/j.csda.2013.02.005.

Hawkes, Alan G. 1971. “Spectra of Some Self-Exciting and Mutually Exciting Point Processes.” Biometrika 58 (1): 83–90. https://doi.org/10.2307/2334319.

Hawkes, Alan G., and David Oakes. 1974. “A cluster process representation of a self-exciting process.” J. Appl. Probab. 11 (03): 493–503. https://doi.org/10.2307/3212693.

Johnson, Steven G. n.d. “The NLopt nonlinear-optimization package.”

Ozaki, T., and Yosihiko Ogata. 1979. “Maximum likelihood estimation of Hawkes’ self-exciting point processes.” Ann. Inst. Stat. Math. 31 (1): 145–55. https://doi.org/10.1007/BF02480272.

Paparoditis, Efstathios. 2000. “Spectral density based goodness-of-fit tests for time series models.” Scand. J. Stat. 27 (1): 143–76. https://doi.org/10.1111/1467-9469.00184.

Rowe, B. T. P., M. Jarvis, R. Mandelbaum, G. M. Bernstein, J. Bosch, M. Simet, J. E. Meyers, et al. 2015. “GalSim: The modular galaxy image simulation toolkit.” Astron. Comput. 10: 121–50. https://doi.org/10.1016/j.ascom.2015.02.002.