CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting

Abstract:Accurately modeling cross-variate dependencies remains a key challenge in multivariate time series forecasting, particularly in the presence of strong periodic patterns. Many existing approaches rely on attention-based mechanisms that incur quadratic complexity and scale poorly with increasing numbers of variates. Recent attention-free aggregation models address this issue through linear-complexity core-based interactions, but they do not explicitly leverage the global periodic structure present in the data. To overcome this limitation, we propose CARNet, a Cycle-Conditioned Core Aggregation and Redistribution framework that integrates global recurrent cycle information into efficient core based interaction modeling via Multihead Core Aggregation. Extensive experiments on multiple real-world multivariate forecasting benchmarks demonstrate that CARNet consistently outperforms strong transformer and non-attention baselines across diverse prediction horizons while preserving linear-complexity modeling of cross-variate dependencies.

Accepted at ECML PKDD 2026 (Research Track). Shortlisted for Best Research Track Student Paper Award

Machine Learning (cs.LG)

arXiv:2607.21681 [cs.LG]

(or arXiv:2607.21681v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.21681 Focus to learn more arXiv-issued DOI via DataCite

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