ArticleNature communications2024
Predicting multiple observations in complex systems through low-dimensional embeddings.
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 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Forecasting extreme temperature events by Hankel-augmented contrastive learning.National science review · 2026Article
- Delayformer: Spatiotemporal Transformation for Predicting High-Dimensional Dynamics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Tree-based learning for high-fidelity prediction of chaos.Scientific reports · 2025Article
- Early warning of regime switching in a complex financial system from a spillover network dynamic perspective.iScience · 2025Article
- Time Persistence of the FMRI Resting-State Functional Brain Networks.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Transforming Prediction into Decision: Leveraging Transformer-Long Short-Term Memory Networks and Automatic Control for Enhanced Water Treatment Efficiency and Sustainability.Sensors (Basel, Switzerland) · 2025Article
- Quantifying the Resilience of Coal Energy Supply in China Toward Carbon Neutrality.Research (Washington, D.C.) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Forecasting all components in complex systems is an open and challenging task, possibly due to high dimensionality and undesirable predictors. We bridge this gap by proposing a data-driven and model-free framework, namely, feature-and-reconstructed manifold mapping (FRMM), which is a combination of feature embedding and delay embedding. For a high-dimensional dynamical system, FRMM finds its topologically equivalent manifolds with low dimensions from feature embedding and delay embedding and then sets the low-dimensional feature manifold as a generalized predictor to achieve predictions of all components. The substantial potential of FRMM is shown for both representative models and real-world data involving Indian monsoon, electroencephalogram (EEG) signals, foreign exchange market, and traffic speed in Los Angeles Country. FRMM overcomes the curse of dimensionality and finds a generalized predictor, and thus has potential for applications in many other real-world systems.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.