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arXiv Machine Learning By Guo Cheng, Zhengzhuo Xu, Chenchen Jing, Jingyi Hou

SACQ: Structured Decoding with Memory-Conditioned Refinement for Long-Horizon Forecasting

Read the original on arXiv Machine Learning →

SACQ introduces a structured prediction head for long‑horizon time‑series forecasting that replaces the traditional flatten readout. It uses a two‑stage decoding process: a coarse patch‑grid scaffold is first generated, then each future position is refined via cross‑attention over historical memory, with a learned gate merging the corrections. A batch‑adaptive scaled log‑cosh loss further stabilizes training under long horizons and noisy labels, leading to superior MSE/MAE performance across multiple backbones with minimal overhead.

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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