IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-46598-w.html
   My bibliography  Save this article

Predicting multiple observations in complex systems through low-dimensional embeddings

Author

Listed:
  • Tao Wu

    (Chengdu University of Technology)

  • Xiangyun Gao

    (China University of Geosciences
    Ministry of Land and Resources)

  • Feng An

    (Beijing University of Chemical Technology)

  • Xiaotian Sun

    (China University of Geosciences)

  • Haizhong An

    (China University of Geosciences
    Ministry of Land and Resources)

  • Zhen Su

    (Potsdam Institute for Climate Impact Research (PIK)–Member of the Leibniz Association
    Humboldt University at Berlin)

  • Shraddha Gupta

    (Potsdam Institute for Climate Impact Research (PIK)–Member of the Leibniz Association
    Humboldt University at Berlin)

  • Jianxi Gao

    (Rensselaer Polytechnic Institute
    Rensselaer Polytechnic Institute)

  • Jürgen Kurths

    (Potsdam Institute for Climate Impact Research (PIK)–Member of the Leibniz Association
    Humboldt University at Berlin)

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.

Suggested Citation

  • Tao Wu & Xiangyun Gao & Feng An & Xiaotian Sun & Haizhong An & Zhen Su & Shraddha Gupta & Jianxi Gao & Jürgen Kurths, 2024. "Predicting multiple observations in complex systems through low-dimensional embeddings," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46598-w
    DOI: 10.1038/s41467-024-46598-w
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-46598-w
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-46598-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jianxi Gao & Baruch Barzel & Albert-László Barabási, 2016. "Erratum: Universal resilience patterns in complex networks," Nature, Nature, vol. 536(7615), pages 238-238, August.
    2. Pei Chen & Rui Liu & Kazuyuki Aihara & Luonan Chen, 2020. "Autoreservoir computing for multistep ahead prediction based on the spatiotemporal information transformation," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
    3. Jianxi Gao & Baruch Barzel & Albert-László Barabási, 2016. "Universal resilience patterns in complex networks," Nature, Nature, vol. 530(7590), pages 307-312, February.
    4. Daniel J. Gauthier & Erik Bollt & Aaron Griffith & Wendson A. S. Barbosa, 2021. "Next generation reservoir computing," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liang, Zhenglin & Li, Yan-Fu, 2023. "Holistic Resilience and Reliability Measures for Cellular Telecommunication Networks," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Dongli, Duan & Chengxing, Wu & Yuchen, Zhai & Changchun, Lv & Ning, Wang, 2022. "Coexistence mechanism of alien species and local ecosystem based on network dimensionality reduction method," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    3. Tu, Chengyi & Fan, Ying & Shi, Tianyu, 2024. "Dimensionality reduction of networked systems with separable coupling-dynamics: Theory and applications," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    4. Gangwal, Utkarsh & Singh, Mayank & Pandey, Pradumn Kumar & Kamboj, Deepak & Chatterjee, Samrat & Bhatia, Udit, 2022. "Identifying early-warning indicators of onset of sudden collapse in networked infrastructure systems against sequential disruptions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    5. Meng, Xiangyi & Zhou, Bin, 2023. "Scale-free networks beyond power-law degree distribution," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    6. Chunheng Jiang & Zhenhan Huang & Tejaswini Pedapati & Pin-Yu Chen & Yizhou Sun & Jianxi Gao, 2024. "Network properties determine neural network performance," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    7. Alejandro Martínez-Calvo & Matthew D. Biviano & Anneline H. Christensen & Eleni Katifori & Kaare H. Jensen & Miguel Ruiz-García, 2024. "The fluidic memristor as a collective phenomenon in elastohydrodynamic networks," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    8. Che, Yiming & Zhang, Ziang (John) & Cheng, Changqing, 2023. "Physical–statistical learning in resilience assessment for power generation systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    9. Aura Reggiani, 2022. "The Architecture of Connectivity: A Key to Network Vulnerability, Complexity and Resilience," Networks and Spatial Economics, Springer, vol. 22(3), pages 415-437, September.
    10. Dui, Hongyan & Liu, Meng & Song, Jiaying & Wu, Shaomin, 2023. "Importance measure-based resilience management: Review, methodology and perspectives on maintenance," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    11. Schipfer, F. & Mäki, E. & Schmieder, U. & Lange, N. & Schildhauer, T. & Hennig, C. & Thrän, D., 2022. "Status of and expectations for flexible bioenergy to support resource efficiency and to accelerate the energy transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    12. Rocchetta, Roberto, 2022. "Enhancing the resilience of critical infrastructures: Statistical analysis of power grid spectral clustering and post-contingency vulnerability metrics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    13. Radillo-Ochoa, Diego & Rodríguez-Hernández, Andrea & Terrero-Escalante, César A., 2023. "Bifurcation in cellular evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    14. Kovács, Olivér, 2024. "A reziliencia metamorfózisa [The metamorphosis of resilience]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(4), pages 408-443.
    15. Xu, Renjie & Liu, Jiahao & Li, Jichao & Yang, Kewei & Zio, Enrico, 2024. "TSoSRA: A task-oriented resilience assessment framework for system-of-systems," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    16. Wu, Chengxing & Duan, Dongli, 2024. "Collapse process prediction of mutualistic dynamical networks with k-core and dimension reduction method," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    17. Roy Cerqueti & Giulia Rotundo, 2023. "The weighted cross-shareholding complex network: a copula approach to concentration and control in financial markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(2), pages 213-232, April.
    18. Kuhn, Moritz & Luo, Jinfeng & Manovskii, Iourii & Qiu, Xincheng, 2023. "Coordinated firm-level work processes and macroeconomic resilience," Journal of Monetary Economics, Elsevier, vol. 137(C), pages 107-127.
    19. Wang, Xinglong & Peng, Jinhan & Tang, Junqing & Lu, Qiuchen & Li, Xiaowei, 2022. "Investigating the impact of adding new airline routes on air transportation resilience in China," Transport Policy, Elsevier, vol. 125(C), pages 79-95.
    20. Chao, Xiangrui & Ran, Qin & Chen, Jia & Li, Tie & Qian, Qian & Ergu, Daji, 2022. "Regulatory technology (Reg-Tech) in financial stability supervision: Taxonomy, key methods, applications and future directions," International Review of Financial Analysis, Elsevier, vol. 80(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46598-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.