AI 中文总结
针对多变量时间序列预测的参数效率与灵活性两难，本文提出非对称时间算子 AsyTO，通过分解算子张量实现参数线性增长，在多数基准场景中达到最优轻量级误差并处于帕累托前沿。
AI 中文摘要
多变量时间序列预测面临结构性两难：在变量间共享一个时间预测器虽参数高效，但会迫使异质变量通过相同的历史到未来映射进行处理；而针对每个变量学习独立的预测器虽能恢复灵活性，但其计算成本会随变量数量、上下文长度和预测 horizon 的乘积增长。本文认为，若将压缩对象从观测序列改为预测算子，该两难问题即可解决。通过在标准基准上检验各变量的线性历史到未来映射，我们发现，在多数检验场景中，锁相季节分量结合紧凑残差算子的表现优于密集无相位参考算子。残差传输还具有方向性：滞后不变的替代方案始终逊于非对称历史到未来映射。基于此结构，本文提出 AsyTO（非对称时间算子），该算子将各变量算子的张量分解为共享但不同的历史读取和未来写入时间模式，并结合各变量的模式级增益，辅以低秩周期原型和时间模式的周期可分离分解。每个预测仅读取自身变量的历史，因此参数和计算量随变量数量线性增长。在 11 个基准和多个预测 horizon 上，AsyTO 在 44 个数据集-horizon 场景中的 30 个场景达到了最佳轻量级误差，处于精度-计算帕累托前沿。
英文摘要
Multivariate time-series forecasting faces a structural dilemma: sharing one temporal predictor across variables is parameter-efficient but forces heterogeneous variables through an identical history-to-future map, whereas learning an independent predictor per variable restores flexibility at a cost that grows with the product of variable count, context length, and horizon. We argue that this dilemma dissolves once the object being compressed is the forecasting operator rather than the observed series. Auditing per-variable linear history-to-future maps across standard benchmarks, we find that a phase-locked seasonal component paired with a compact residual operator outperforms a dense phase-blind reference in most audited settings. The residual transport is also directional: lag-invariant alternatives consistently underperform asymmetric history-to-future maps. Guided by this structure, we propose AsyTO, an Asymmetric Temporal Operator that factorizes the tensor of per-variable operators into shared but distinct history-reading and future-writing temporal modes with per-variable mode-wise gains, complemented by a low-rank periodic prototype and a cycle-separable factorization of the temporal modes. Each forecast reads only its own variable's history, so parameters and compute grow linearly in the number of variables. Across eleven benchmarks and multiple forecast horizons, AsyTO attains the best lightweight error in 30 of 44 dataset-horizon settings, locating at the accuracy-compute Pareto frontier.
Comments8 pages, 4 figures, 4 tables
