论文标题
关于时间序列建模的训练时间与可解释性之间的平衡
On the balance between the training time and interpretability of neural ODE for time series modelling
论文作者
论文摘要
大多数机器学习方法被用作用于建模的黑匣子。我们可能会尝试从基于物理的训练方法中提取一些知识，例如神经颂（普通微分方程）。神经ODE具有可能更高的代表函数类别，与黑盒机器学习模型相比，可以扩展的可解释性，描述趋势和局部行为的能力。这种优势对于具有复杂趋势的时间序列尤其重要。但是，已知的缺点是与自回归模型和长期术语内存（LSTM）网络相比，广泛用于数据驱动的时间序列建模的高训练时间。因此，我们应该能够平衡可解释性和训练时间，以在实践中应用神经颂歌。该论文表明，现代神经颂歌不能简化为时间序列建模应用程序的模型。将神经ODE的复杂性与传统的时间序列建模工具进行比较。可以提取的唯一解释是操作员的特征空间，这对于大型系统来说是一个不适的问题。可以使用不同的经典分析方法提取光谱，而这些方法没有延长时间的缺点。因此，我们将神经ODE缩小为更简单的线性形式，并使用组合神经网络和ODE系统方法对时间序列建模进行新的视图。
Most machine learning methods are used as a black box for modelling. We may try to extract some knowledge from physics-based training methods, such as neural ODE (ordinary differential equation). Neural ODE has advantages like a possibly higher class of represented functions, the extended interpretability compared to black-box machine learning models, ability to describe both trend and local behaviour. Such advantages are especially critical for time series with complicated trends. However, the known drawback is the high training time compared to the autoregressive models and long-short term memory (LSTM) networks widely used for data-driven time series modelling. Therefore, we should be able to balance interpretability and training time to apply neural ODE in practice. The paper shows that modern neural ODE cannot be reduced to simpler models for time-series modelling applications. The complexity of neural ODE is compared to or exceeds the conventional time-series modelling tools. The only interpretation that could be extracted is the eigenspace of the operator, which is an ill-posed problem for a large system. Spectra could be extracted using different classical analysis methods that do not have the drawback of extended time. Consequently, we reduce the neural ODE to a simpler linear form and propose a new view on time-series modelling using combined neural networks and an ODE system approach.
