Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras

Last update: Jan 14, 2022

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Overview

neural_calibrator

Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras which will then be used to generate residuals. I dont think this is great code but i think that is something for people to start with and works for my use case.

currently supports:

CoxIngersollRoss Process
Diffusion Process
Fractional Brownian Motion
Geometric Brownian Motion
Ornstein Uhlenbeck Process
Poisson Process
Vasicek Process

what's what:

- data_generation.py -> handles data (sample, config, saving to csv for post analysis, train/test split, ...)
- main.py -> this is where you f5 and follow instructions to get the trained .h5 model to load elsewhere
- network.py -> basically the network we'll train, test and save if needed. kept as clean as possible so you can copy/paste most of this elsewhere.
- network_manager.py -> handles the actual heavy stuff like training/testing/tensorboard/etc. made for extension but not much work put into it.
- start_here.ipynb -> small notebook with examples

comming up next:

Markov chains (HMM/Bernoulli chains/etc)
Hawkes process

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Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras

Related tags

Overview

neural_calibrator

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