arXiv Machine Learning July 13, 2026 By Qitai Tan, Ruiwen Gu, Yilin Su, Mo Li, Xu Lin, Xiao-Ping Zhang

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting

Read the original on arXiv Machine Learning →

arXiv:2607. 09537v1 Announce Type: new Abstract: Time series forecasting requires models to capture diverse, often mutually exclusive, temporal dynamics, from smooth trend continuation to nonstationary drift and strict phase-aligned recurrence.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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StateFlow: Dual-State Recurrent Modeling for Long-Horizon Time Series Forecasting

Interpretable deep convolutional model for nonlinear multivariate time series in complex systems

Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting

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