This is the official repository for Liquid TIme-stochasticity networks described in paper: https://doi.org/10.1109/CCWC57344.2023.10099071
This implementation utilizes the Euler Maruyama solver to perform forward propagation and relies on the conventional backpropagation through-time (BPTT) to train the models.
The architecture was built using Keras and TensorFlow 2.0+ and Python 3+ on the Windows 11 machine.
experiments/experiments.ipynb demonstrates a couple of experiments attempting to model bitcoin prices.