[Submitted on 15 Oct 2026]
Title:CGDT-CESN: Correlation-Guided and Dynamics-Trained Clustered Echo State Network for Multivariate Time-Series Prediction
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Abstract:Accurate prediction of multivariate time series requires models that simultaneously capture variable-specific dynamics and cross-variable interactions. In this work, a Correlation-Guided and Dynamics-Trained Clustered Echo State Network (CGDT-CESN) is proposed, which comprises a modular reservoir architecture with each cluster distinctly specializing in one of the variables in the multivariate time series. An innovative reservoir design is proposed in which inter-cluster connections are enabled by empirically observed correlations among variables, providing a data-driven coupling mechanism. Apart from conventional readout training, inter-cluster connection weights informed by correlations are trained, which in turn allows the reservoir to capture time-varying cross-variable dependencies without sacrificing computational efficiency and further improves forecasting accuracy. The prediction performance of CGDT-CESN is found to be superior to that of the conventional ESN when evaluated on two fundamentally different datasets: Twitter information cascade evolution and solar wind forecasting. Also, the improvement in performance is preserved across multiple prediction horizons, even when presented with a low volume of data. By unifying cluster-specific specialization with learned inter-variable coupling, CGDT-CESN offers a principled, data-driven route to multivariate time series prediction.
Subjects:
Machine Learning (cs.LG); Adaptation and Self-Organizing Systems (nlin.AO)
Cite as: arXiv:2610.22430 [cs.LG]
(or arXiv:2610.22430v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.22430
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hariharan Srinivasan [view email] [v1] Thu, 15 Oct 2026 07:32:37 UTC (2,514 KB)
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