论文标题
逐渐生长神经氧
Progressive Growing of Neural ODEs
论文作者
论文摘要
神经普通微分方程（节点）已被证明是用于近似（插值）和预测（外推）不规则采样时间序列数据的强大建模工具。但是，当应用于现实世界中的数据，尤其是具有复杂行为的长期数据时（例如，多年来的长期趋势，跨月的中期季节性以及在几天之间的短期局部变化）时，它们的性能会大大降低。为了以不同频率（时间跨度）的不同行为来解决此类复杂数据的建模，我们提出了一个新型的长期时间序列节点的渐进式学习范式。具体而言，遵循课程学习的原则，随着培训的进展，我们逐渐增加了数据和网络容量的复杂性。我们使用合成数据和真实流量数据（PEMS湾区交通数据）进行的实验表明，我们的培训方法一致地将香草节点的性能提高了64％以上。
Neural Ordinary Differential Equations (NODEs) have proven to be a powerful modeling tool for approximating (interpolation) and forecasting (extrapolation) irregularly sampled time series data. However, their performance degrades substantially when applied to real-world data, especially long-term data with complex behaviors (e.g., long-term trend across years, mid-term seasonality across months, and short-term local variation across days). To address the modeling of such complex data with different behaviors at different frequencies (time spans), we propose a novel progressive learning paradigm of NODEs for long-term time series forecasting. Specifically, following the principle of curriculum learning, we gradually increase the complexity of data and network capacity as training progresses. Our experiments with both synthetic data and real traffic data (PeMS Bay Area traffic data) show that our training methodology consistently improves the performance of vanilla NODEs by over 64%.
