AI 中文总结
本研究重新审视时间序列预测中跨通道重要性，证明统计相关、预测有用和实际使用三者不同，并提出地平线自适应源选择与后验支持方法，显著提升预测性能。
AI 中文摘要
跨通道建模是多变量时间序列预测的核心，然而，统计上相关、预测上有用且被训练后的预测器实际使用的通道，往往被当作定义了相同的重要性概念。我们表明，它们不必重合。跨通道依赖结构在不同未来偏移下发生显著变化，且自适应地平线的源选择在32个数据集-预测长度条件中的21个中改善了受控的Ridge预测器，平均增益为5.16%。该选定集信号也迁移到匹配的非线性预测器。然而，将相同的特定地平线源逻辑强加于iTransformer仅获得20次中的11次胜利，平均增益为0.208%，且受控增益与神经增益之间几乎没有对齐。功能干预进一步表明，强预测器使用跨通道信息，而它们的源依赖排名与受控效用或彼此之间在iTransformer、TimesNet和跨通道TimeMixer中几乎没有一致性。作为建设性结果，有界的后验支持在16个数据集-地平线条件中的12个中改善了冻结的通道独立预测器，并具有正的聚合自助置信区间。因此，跨通道重要性应相对于定义它的预测机制和问题来解释：相关不等于有用，有用不等于使用。
英文摘要
Cross-channel modeling is central to multivariate time-series forecasting, yet channels that are statistically related, predictively useful, and actually used by a trained forecaster are often treated as if they defined the same notion of importance. We show that they need not coincide. Cross-channel dependency structures change substantially across future offsets, and horizon-adaptive source selection improves a controlled Ridge predictor in 21 of 32 dataset--prediction-length conditions, with a mean gain of $5.16\%$. This selected-set signal also transfers to a matched nonlinear predictor. Yet imposing the same horizon-specific source logic on iTransformer yields only 11 of 20 wins and a mean gain of $0.208\%$, with little alignment between controlled and neural gains. Functional interventions further show that strong forecasters use cross-channel information, while their source-reliance rankings agree little with controlled utility or with one another across iTransformer, TimesNet, and a cross-channel TimeMixer. As a constructive consequence, bounded post-hoc support improves a frozen channel-independent forecaster in 12 of 16 dataset--horizon conditions, with a positive aggregate bootstrap interval. Cross-channel importance should therefore be interpreted relative to the forecasting mechanism and question that define it: related $\neq$ useful $\neq$ used.
