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Updated: Jun 29, 2025

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DEFM:通过时空信息转换进行时间序列预测的基于延迟嵌入的预测机器

Hao Peng1, Wei Wang1, Pei Chen1

  • 1School of Mathematics, South China University of Technology, Guangzhou 510640, China.

Chaos (Woodbury, N.Y.)
|April 4, 2024
PubMed
概括

本研究引入了一种新的框架,即基于延迟嵌入的预测机器 (DEFM),用于复杂系统中的准确预测. 使用深度学习和延迟嵌入理论,DEFM有效地预测非线性时空动态中的未来值.

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The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
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In signal processing, Discrete-Time Fourier Transforms (DTFTs) play a critical role in analyzing discrete-time signals in the frequency domain. Various properties of the DTFTs such as linearity, time-shifting, frequency-shifting, time reversal, conjugation, and time scaling help understand and manipulate these signals for different applications.
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