论文标题
使用一致的Koopman自动编码器预测顺序数据
Forecasting Sequential Data using Consistent Koopman Autoencoders
论文作者
论文摘要
复发性神经网络被广泛用于时间序列数据，但是这些模型通常会忽略此类序列中的基本物理结构。引入了与Koopman理论相关的一种新的基于物理学的方法，为处理非线性动力学系统提供了替代方法。在这项工作中，我们提出了一种新颖的Koopman AutoCododer模型，该模型与大多数现有作品不同，它利用了前进和向后的动态。我们方法的关键是一项新的分析，该分析探讨了一致的动态与其相关的Koopman操作员之间的相互作用。我们的网络与派生分析直接相关，其计算要求与其他基线相媲美。我们在广泛的高维和短期依赖性问题上评估了我们的方法，并对明显的预测范围进行了准确的估计，同时对噪声也有稳定性。
Recurrent neural networks are widely used on time series data, yet such models often ignore the underlying physical structures in such sequences. A new class of physics-based methods related to Koopman theory has been introduced, offering an alternative for processing nonlinear dynamical systems. In this work, we propose a novel Consistent Koopman Autoencoder model which, unlike the majority of existing work, leverages the forward and backward dynamics. Key to our approach is a new analysis which explores the interplay between consistent dynamics and their associated Koopman operators. Our network is directly related to the derived analysis, and its computational requirements are comparable to other baselines. We evaluate our method on a wide range of high-dimensional and short-term dependent problems, and it achieves accurate estimates for significant prediction horizons, while also being robust to noise.
