All Projects → ManifoldFR → point-process-nets

ManifoldFR / point-process-nets

Licence: MIT License
Point processes backed by neural net intensity models

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Neural network-based point process models

Synthetic point processes are simulated using tick and point-process-rust.

Neural networks are written using PyTorch.

Objective

Use a modified neural network-based Hawkes process model for next event prediction in a time series.

Overview

Directories:

  • data contains the data
  • biblio contains the bibliography
  • rapport contains the project report
  • notebooks contains the Jupyter notebooks

Installation

Loading a model

From Saving & Loading models.
If the model state dict was saved with torch.save(model.state_dict(), PATH)

import torch
from models import ModelClass

model = ModelClass(*args, **kwargs)
model.load_state_dict(torch.load(PATH))
model.eval() # Evaluation mode

References

  1. Second order statistics characterization of Hawkes processes and non-parametric estimation (arXiv :1401.0903) E. Bacry, J.F. Muzy. Trans. in Inf. Theory, 62, Iss.4 (2016) https://arxiv.org/abs/1401.0903
  2. Estimation of slowly decreasing Hawkes kernels : Application to high frequency order book modelling (arXiv :1412.7096) E.Bacry, T.Jaisson, J.-F.Muzy Quantitative Finance Vol.16 Iss. 8 (2016) https://arxiv.org/abs/1412.7096
  3. Hawkes processes in finance. (arXiv :1502.04592) E.Bacry, I.Mastromatteo, J.-F.Muzy Market Microstructure and Liquidity Vol. 01, No. 01, 1550005 (2015). https://arxiv.org/abs/1502.04592
  4. The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process, H. Mei, J. Eisner https://arxiv.org/pdf/1612.09328.pdf
  5. Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks https://arxiv.org/pdf/1705.08982.pdf
  6. On a Bayesian RNN for learning the decrease speed parameter in a process: Neural Hawkes Process Memory (Mike Mozer) http://www.cs.colorado.edu/~mozer/Research/Selected%20Publications/talks/Mozer_NeuralHawkesProcessMemory_NIPS2016.pdf
  7. Recurrent Marked Temporal Point Processes: https://www.kdd.org/kdd2016/papers/files/rpp1081-duA.pdf
  8. Numerical Recipes The Art of scientific Computing (An amazing book for not analytic integration) https://e-maxx.ru/bookz/files/numerical_recipes.pdf
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