ArticleNature communications2024
Higher-order Granger reservoir computing: simultaneously achieving scalable complex structures inference and accurate dynamics prediction.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Advances in causal discovery methods for ecological time series.Biological reviews of the Cambridge Philosophical Society · 2026Review
- HNPP: Higher-order network-based personalized PageRank for detecting critical phase in complex biological systems.PLoS computational biology · 2026Article
- Deciphering optical coupled resonant systems with physics-data co-driven deep neural networks.Light, science & applications · 2026Article
- Self-supervised reservoir computing with spatial-temporal encoding for identifying critical transitions.Nature communications · 2026Article
- Data-driven dynamic modeling for inverter-based resources using neural networks.Nature communications · 2025Article
- Modeling nonlinear oscillator networks using physics-informed hybrid reservoir computing.Scientific reports · 2025Article
- How more data can hurt: Instability and regularization in next-generation reservoir computing.Chaos (Woodbury, N.Y.) · 2025Article
- Deep learning for predicting the occurrence of tipping points.Royal Society open science · 2025Article
- Hypergraph reconstruction from dynamics.Nature communications · 2025Article
- Predicting three-dimensional chaotic systems with four qubit quantum systems.Scientific reports · 2025Article
- Learning interpretable dynamics of stochastic complex systems from experimental data.Nature communications · 2024Article
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Authors and funding
9 authors.
Funding
Abstract
Recently, machine learning methods, including reservoir computing (RC), have been tremendously successful in predicting complex dynamics in many fields. However, a present challenge lies in pushing for the limit of prediction accuracy while maintaining the low complexity of the model. Here, we design a data-driven, model-free framework named higher-order Granger reservoir computing (HoGRC), which owns two major missions: The first is to infer the higher-order structures incorporating the idea of Granger causality with the RC, and, simultaneously, the second is to realize multi-step prediction by feeding the time series and the inferred higher-order information into HoGRC. We demonstrate the efficacy and robustness of the HoGRC using several representative systems, including the classical chaotic systems, the network dynamical systems, and the UK power grid system. In the era of machine learning and complex systems, we anticipate a broad application of the HoGRC framework in structure inference and dynamics prediction.
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Registered trials
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