Improved Batching Strategy For Irregular Time-Series ODE

TL;DR

This paper introduces an improved batching strategy for ODE-RNN models that significantly reduces runtime while maintaining accuracy, enabling more efficient modeling of irregular time-series data.

Contribution

The paper proposes a novel batching method that enhances the efficiency of ODE-RNNs for irregular time-series data, reducing computational costs substantially.

Findings

Runtime reduced by up to 49 times

Maintains comparable accuracy to original models

Scales effectively for larger datasets

Abstract

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.