EPOC：基于压缩残差状态的端点保持在线校正用于多步时间序列预测
EPOC: Endpoint-Preserving Online Correction With Compressed Residual State for Multi-Horizon Time Series Forecasting
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中文总结 AI 辅助
EPOC通过压缩残差状态（存储DCT系数和端点）进行在线校正，在多步预测中平均降低MSE 15.40%，且辅助状态远小于完整方法。
中文摘要 AI 辅助
已完成的多步预测为固定预测器提供残差反馈，但保留完整残差块会增加辅助状态。我们提出带有压缩残差状态的端点保持在线校正（EPOC）。它存储前一个残差块的低阶离散余弦变换（DCT）系数和最终值。在每个通道内，端点被共享于使用当前预测系数的逐分量在线岭回归。拟合的DCT校正与基础预测混合。我们使用DLinear和PatchTST评估八个多变量序列，三个种子和两种训练变体，在24步预测范围内产生96个匹配的固定基础条件。EPOC在均方误差（MSE）和平均绝对误差（MAE）上分别实现平均条件减少15.40%和9.35%，相对于未校正的基础，保留辅助数组的中位数为6,352字节。在大多数条件下，其配对MSE低于δ-Adapter、COSA、FAC和OMPB，并且使用的状态少于每种方法。完整的ELF实现最大的平均MSE减少，为19.29%，但其保留状态中位数为474,048字节（相对于EPOC的75倍）。等大小摘要控制偏向端点，配对MSE改善1.65-2.20%；系数重建端点产生与观测端点相似的精度，突出其作为共享输入的作用。将保留的DCT分量数从4增加到8，为5,728字节增加1.00个百分点的MSE减少。在联合训练基础上，EPOC相对于应用于相同基础的全局混合TEFL风格适配器，将MSE降低16.69-20.15%。代码和数值记录可在该https URL获取。
英文摘要
Completed forecasts provide residual feedback, but retaining full residual blocks increases auxiliary state. We propose Endpoint-Preserving Online Correction (EPOC) with a compressed residual state. It stores low-order discrete cosine transform (DCT) coefficients and the final value of the preceding residual block. Each channel shares the endpoint across component-wise online ridge regressions using current-forecast coefficients. We evaluate eight multivariate series, DLinear and PatchTST, three seeds, and two training variants: 96 matched fixed-base conditions at a 24-step horizon. EPOC achieves mean condition-wise reductions in mean squared error (MSE) and mean absolute error (MAE) of 15.40% and 9.35% from the uncorrected base, respectively, with a median of 6,352 B in retained auxiliary arrays. EPOC also outperforms the $δ$-Adapter, COSA, FAC, and OMPB in paired MSE on most conditions while retaining less state. Full ELF achieves the largest mean MSE reduction, 19.29%, but its median retained state is 474,048 B ($\times$75 relative to EPOC). Equal-size summary controls favor the endpoint by 1.65--2.20% in paired MSE; a coefficient-reconstructed endpoint yields similar accuracy to the observed endpoint, highlighting its role as a shared input. Increasing the retained DCT component count from 4 to 8 adds 1.00 percentage point of MSE reduction for 5,728 B. On jointly trained bases, EPOC lowers MSE by 16.69--20.15% relative to globally blended TEFL-style adapters applied to the same base. Code and numerical records are available at [https://github.com/keiotakmin/endpoint-preserving-residual-correction](https://github.com/keiotakmin/endpoint-preserving-residual-correction).
发表机构
- Keio University（庆应义塾大学）
机构由 AI 辅助整理，请以论文原文为准。
