Non-Negative Online Estimation for Hawkes Process Networks

Marc J. Piggott; Victor Solo

ICASSP 2018accepted0 citations

Non-Negative Online Estimation for Hawkes Process Networks

Abstract

Networks of interacting Hawkes processes have emerged as useful models in neuroscience, geophysics, high frequency finance, and social network analysis. The Hawkes process is of fundamental importance, being a point process analog of an autoregression. Here we develop a fixed gain adaptive (aka online) distributed estimator for the parameters of a Hawkes process model. The stochastic intensity is modeled by a causal Laguerre basis expansion. The natural recursive structure of this basis is exploited to derive a new two time scale adaptive algorithm based on exponentially weighted least squares which preserves non-negativity constraints. Simulations illustrate the results.

BibTeX

@inproceedings{icassp2018_nonnegativeonlin,
  title = {Non-Negative Online Estimation for Hawkes Process Networks},
  author = {Marc J. Piggott and Victor Solo},
  booktitle = {ICASSP 2018},
  year = {2018}
}