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- W2765141023 abstract "Natural systems are typically nonlinear and complex, and it is of great interest to be able to reconstruct a system in order to understand its mechanism, which can not only recover nonlinear behaviors but also predict future dynamics. Due to the advances of modern technology, big data becomes increasingly accessible and consequently the problem of reconstructing systems from measured data or time series plays a central role in many scientific disciplines. In recent decades, nonlinear methods rooted in state space reconstruction have been developed, and they do not assume any model equations but can recover the dynamics purely from the measured time series data. In this review, the development of state space reconstruction techniques will be introduced and the recent advances in systems prediction and causality inference using state space reconstruction will be presented. Particularly, the cutting-edge method to deal with short-term time series data will be focused. Finally, the advantages as well as the remaining problems in this field are discussed." @default.
- W2765141023 created "2017-11-10" @default.
- W2765141023 creator A5003378348 @default.
- W2765141023 creator A5012283696 @default.
- W2765141023 creator A5047868854 @default.
- W2765141023 date "2017-10-31" @default.
- W2765141023 modified "2023-09-30" @default.
- W2765141023 title "Data-based prediction and causality inference of nonlinear dynamics" @default.
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- W2765141023 doi "https://doi.org/10.48550/arxiv.1710.11318" @default.
- W2765141023 hasPublicationYear "2017" @default.
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