论文标题
改进了不规则时间序列ode的批处理策略
Improved Batching Strategy For Irregular Time-Series ODE
论文作者
论文摘要
不规则的时间序列数据在现实世界中很普遍，并且具有简单的复发性神经网络（RNN）的建模具有挑战性。因此，提出了一种结合使用普通微分方程（ODE）和RNN使用的模型（ODE-RNN），以更高的精度对不规则时间序列进行建模，但其计算成本很高。在本文中，我们通过使用不同的有效批处理策略提出了ODE-RNN的运行时间的改进。我们的实验表明，新模型将ODE-RNN的运行时间显着从2次降低到49次，这取决于数据的不规则性，同时保持可比的精度。因此，我们的模型可以对建模较大的不规则数据集建模。
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.
