Neural Controlled Differential Equations
for Irregular Time Series
[arXiv]
Building on the well-understood mathematical theory of controlled differential equations, we demonstrate how to construct models that:
- Act directly on irregularly-sampled partially-observed multivariate time series.
- May be trained with memory-efficient adjoint backpropagation - even across observations.
- Demonstrate state-of-the-art performance.
They are straightforward to implement and evaluate using existing tools, in particular PyTorch and the torchdiffeq
library.
Code for reproducing experiments is provided, as well as a convenience library controldiffeq
to make computing Neural CDEs easy.
The library is in the controldiffeq
folder, which may be imported as a Python module: import controldiffeq
. Check the folder for details on how to use it.
An example can be found here, which demonstrates how to train a Neural CDE to detect the chirality (clockwise/anticlockwise) of a spiral.
Everything to reproduce the experiments of the paper can be found in the experiments
folder. Check the folder for details.
As an example (taken from the paper - have a look there for similar results on other datasets):
@article{kidger2020neuralcde,
author={Kidger, Patrick and Morrill, James and Foster, James and Lyons, Terry},
title={{Neural Controlled Differential Equations for Irregular Time Series}},
year={2020},
journal={arXiv:2005.08926}
}