Improved Batching Strategy For Irregular Time-Series ODE

• URL: http://arxiv.org/abs/2207.05708v1
• Date: Tue, 12 Jul 2022 17:30:02 GMT
• Title: Improved Batching Strategy For Irregular Time-Series ODE
• Authors: Ting Fung Lam, Yony Bresler, Ahmed Khorshid and Nathan Perlmutter
• Abstract summary: We propose an improvement in the runtime on ODE-RNNs by using a different efficient 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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