Reconstruction and Prediction of Chaotic Time Series with Missing Data: Leveraging Dynamical Correlations Between Variables

Reconstruction and Prediction of Chaotic Time Series with Missing Data: Leveraging Dynamical Correlations Between Variables

Although data-driven machine learning methods have been successfully applied to predict complex nonlinear dynamics, forecasting future evolution based on incomplete past information remains a significant challenge. This paper proposes a novel data-driven approach that leverages the dynamical relatio...

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Bibliographic Details
Main Authors: Jingchan Lv, Hongcun Mao, Yu Wang, Zhihai Yao
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Mathematics
Subjects:
chaotic time series prediction
missing data reconstruction
multivariate time series
dynamical correlation
machine learning
Online Access:https://www.mdpi.com/2227-7390/13/1/152
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