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arXiv Machine Learning By Hongkai Zhuang, Tao Huang, Chen Hou

A Lightweight Plug-in Gate for Transformer-Based Time-Series Forecasters

Read the original on arXiv Machine Learning →

The paper introduces a lightweight pre‑encoder gate for Transformer‑based time‑series forecasters, which assigns sigmoid scores to covariate representations before they enter the encoder. The gate is evaluated as a plug‑in for models such as TimeXer, iTransformer, and PatchTST on datasets including ETTm1, ETTm2, Traffic, Energy, and ILI, showing competitive performance and the ability to regulate covariate admission via a usage penalty. Experiments also explore gate placement, initialization, and feature importance using VIF‑informed permutation diagnostics.

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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