Python framework for inference in Hawkes processes. PyHawkes implements a variety of Bayesian inference algorithms for discovering latent network structure given point process observations. Suppose you observe timestamps of Twitter messages, but you don’t get to see how those users are connected to one another. You might infer that there is an unobserved connection from one user to another if the first user’s activity tends to precede the second user’s. This intuition is formalized by combining excitatory point processes (aka Hawkes processes) with random network models and performing Bayesian inference to discover the latent network.

References in zbMATH (referenced in 13 articles )

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  1. Molkenthin, Christian; Donner, Christian; Reich, Sebastian; Zöller, Gert; Hainzl, Sebastian; Holschneider, Matthias; Opper, Manfred: GP-ETAS: semiparametric Bayesian inference for the spatio-temporal epidemic type aftershock sequence model (2022)
  2. Bonnet, Anna; Martinez Herrera, Miguel; Sangnier, Maxime: Maximum likelihood estimation for Hawkes processes with self-excitation or inhibition (2021)
  3. Holbrook, Andrew J.; Loeffler, Charles E.; Flaxman, Seth R.; Suchard, Marc A.: Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data (2021)
  4. Tiwaskar, Manoj; Garg, Yash; Li, Xinsheng; Candan, K. Selçuk; Sapino, Maria Luisa: Selego: robust variate selection for accurate time series forecasting (2021)
  5. White, Philip A.; Gelfand, Alan E.: Generalized evolutionary point processes: model specifications and model comparison (2021)
  6. Bacry, Emmanuel; Bompaire, Martin; Gaïffas, Stéphane; Muzy, Jean-Francois: Sparse and low-rank multivariate Hawkes processes (2020)
  7. Price-Williams, Matthew; Heard, Nicholas A.: Nonparametric self-exciting models for computer network traffic (2020)
  8. Koyama, Shinsuke; Fujiwara, Yoshi: Modeling event cascades using networks of additive count sequences (2019)
  9. Metelli, Silvia; Heard, Nicholas: On Bayesian new edge prediction and anomaly detection in computer networks (2019)
  10. Yuan, Baichuan; Li, Hao; Bertozzi, Andrea L.; Brantingham, P. Jeffrey; Porter, Mason A.: Multivariate spatiotemporal Hawkes processes and network reconstruction (2019)
  11. Emmanuel Bacry, Martin Bompaire, Stéphane Gaïffas, Soren Poulsen: Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling (2017) arXiv
  12. Hongteng Xu, Hongyuan Zha: THAP: A Matlab Toolkit for Learning with Hawkes Processes (2017) arXiv
  13. Bacry, Emmanuel; Gaïffas, Stéphane; Mastromatteo, Iacopo; Muzy, Jean-François: Mean-field inference of Hawkes point processes (2016)