Irregular time series data are prevalent in the real world and are challenging to model with a simple recurrent neural network (RNN). Hence, a model that combines the use of ordinary differential equations (ODE) and RNN was proposed (ODE-RNN) to model irregular time series with higher accuracy, but it suffers from high computational costs. In this paper, we propose an improvement in the runtime on ODE-RNNs by using a different efficient batching strategy. Our experiments show that the new models reduce the runtime of ODE-RNN significantly ranging from 2 times up to 49 times depending on the irregularity of the data while maintaining comparable accuracy. Hence, our model can scale favorably for modeling larger irregular data sets.
@article{arxiv.2207.05708,
title = {Improved Batching Strategy For Irregular Time-Series ODE},
author = {Ting Fung Lam and Yony Bresler and Ahmed Khorshid and Nathan Perlmutter},
journal= {arXiv preprint arXiv:2207.05708},
year = {2022}
}