Simple linear and frequency-domain models remain surprisingly competitive in long-horizon time-series forecasting, and recent mechanistic evidence suggests that standard forecasting benchmarks may not require the dense superposed representations that make transformers powerful in other domains. This raises a substrate-level question: if the core forecasting operator is often low-complexity and approximately linear, does it need to be implemented as learned digital temporal mixing? We introduce HAMON, a passive diffractive optical forecasting core in which historical values are encoded onto an optical aperture, future positions are left dark, and cascaded trainable phase masks with free-space diffraction shape the forecast directly in the output field. At inference, prediction is performed by a single passive optical propagation pass with no trainable digital sequence-mixing layer. Across standard benchmarks, HAMON outperforms the strongest digital baselines considered on ETTm2 at all horizons and on ETTh2 at all but the longest horizon, improving MSE by up to 14\% and doing so consistently across horizons rather than at isolated points. It is competitive on Weather and trails the strongest baselines on the remaining ETT settings and on the high-channel-count Traffic and Electricity datasets. Phase encoding, intensity-compatible readout, and phase-scrambling ablations, together with a TorchOptics cross-simulator check, indicate that the forecasts arise from the data-bearing optical field rather than from a digital forecasting head. Because the passive core uses standard Fourier optics, HAMON defines a concrete target for optical hardware and for passive physical sequence mixing.

翻译：简单的线性模型和频域模型在长程时间序列预测中依然展现出令人意外的竞争力，而近期机制性证据表明，标准预测基准可能并不需要Transformer在其他领域展现强大性能所需的密集叠加表示。这引发了一个基质层面的问题：若核心预测算子通常具有低复杂度且近似线性，是否必须将其实现为学习到的数字时序混合？我们提出HAMON——一种无源衍射光学预测核心，其中历史数值被编码于光学孔径上，未来位置保持暗态，级联的可训练相位掩模结合自由空间衍射直接在输出场中形成预测。在推理阶段，单个无源光学传播通道即可完成预测，无需任何可训练的数字序列混合层。在标准基准上，HAMON在ETTm2数据集的所有预测长度以及ETTh2数据集除最长预测长度外的所有场景中均优于最强数字基线，MSE提升幅度最高达14%，且性能改善在多个预测长度上具有一致性而非孤立的提升点。其在Weather数据集上具备竞争力，在其余ETT设置及高通道数的Traffic和Electricity数据集上略逊于最强基线。相位编码、与强度兼容的读取方式、相位扰乱消融实验以及基于TorchOptics的交叉模拟验证均表明，预测结果来源于承载数据的光场而非数字预测头。由于无源核心采用标准傅里叶光学原理，HAMON为光学硬件实现与无源物理序列混合确立了具体目标。