论文标题
DEFM：延迟e M型基于时空信息转换的时间序列预测的预测机
DEFM: Delay E mbedding based Forecast Machine for Time Series Forecasting by Spatiotemporal Information Transformation
论文作者
论文摘要
在各种实际应用中，对复杂系统进行准确的预测是一个挑战。解决此类问题的主要困难涉及与时变特征的非线性时空动力学。 Takens的延迟嵌入理论提供了一种将高维空间信息转换为时间信息的方法。在这项工作中，通过结合延迟嵌入理论和深度学习技术，我们提出了一个新颖的框架，基于延迟的预测机（DEFM），以基于高维观测值的自我监督和多阶段的方式来预测目标变量的未来值。使用三模块时空结构，DEFM利用深度神经网络有效地从观察到的时间序列中有效地提取空间和时间相关的信息，即使有时间变化的参数或添加剂噪声。 DEFM可以通过将时空信息转换为目标变量的延迟嵌入来准确预测未来信息。 DEFM的功效和精度通过在三个时空混乱系统中的应用来证实：90维（90D）耦合的Lorenz系统，Lorenz 96系统和库拉莫托 - 瓦什斯基（Kuramoto-Sivashinsky）（KS）方程（ks）方程。此外，在跨越各个字段的六个现实世界数据集上评估了DEFM的性能。具有五种预测方法的比较实验说明了DEFM的优越性和鲁棒性，并显示了DEFM在时间信息挖掘和预测中的巨大潜力
Making accurate forecasts for a complex system is a challenge in various practical applications. The major difficulty in solving such a problem concerns nonlinear spatiotemporal dynamics with time-varying characteristics. Takens' delay embedding theory provides a way to transform high-dimensional spatial information into temporal information. In this work, by combining delay embedding theory and deep learning techniques, we propose a novel framework, Delay-Embedding-based Forecast Machine (DEFM), to predict the future values of a target variable in a self-supervised and multistep-ahead manner based on high-dimensional observations. With a three-module spatiotemporal architecture, the DEFM leverages deep neural networks to effectively extract both the spatially and temporally associated information from the observed time series even with time-varying parameters or additive noise. The DEFM can accurately predict future information by transforming spatiotemporal information to the delay embeddings of a target variable. The efficacy and precision of the DEFM are substantiated through applications in three spatiotemporally chaotic systems: a 90-dimensional (90D) coupled Lorenz system, the Lorenz 96 system, and the Kuramoto-Sivashinsky (KS) equation with inhomogeneity. Additionally, the performance of the DEFM is evaluated on six real-world datasets spanning various fields. Comparative experiments with five prediction methods illustrate the superiority and robustness of the DEFM and show the great potential of the DEFM in temporal information mining and forecasting
