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arXiv Machine Learning By Takumi Fujimoto, Hiroaki Nishi

EPOC: Endpoint-Preserving Online Correction With Compressed Residual State for Multi-Horizon Time Series Forecasting

Read the original on arXiv Machine Learning →

EPOC (Endpoint‑Preserving Online Correction) is a method for multi‑horizon time‑series forecasting that compresses residual information using low‑order discrete cosine transform (DCT) coefficients and the final residual value. It applies channel‑wise online ridge regressions to blend a DCT‑based correction with a base forecast, achieving significant reductions in mean squared error (15.40%) and mean absolute error (9.35%) across 96 fixed‑base conditions while keeping auxiliary state small (median 6,352 B). The approach outperforms several existing adapters and offers a lightweight, endpoint‑focused correction strategy.

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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