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arXiv Machine Learning By Zhenan Yu, Guangxin Jiang, Jiuyun Jiang, Jin Yang

LagNet: Lag-Structured Operators for Long-Term Time Series Forecasting

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LagNet introduces LagLayer, a Toeplitz‑structured operator that ties history‑to‑future connections by relative lag, replacing traditional temporal linear layers in forecasting models. By stacking LagLayers with activations and multiple feature maps, LagNet achieves competitive performance on long‑term time series benchmarks. Sensitivity matrix diagnostics and limited‑data experiments show that LagLayer offers a sample‑efficient alternative to linear layers.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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