Time-series forecasting is fundamental in industrial domains like manufacturing and smart factories. As systems evolve toward automation, models must operate on edge devices (e.g., PLCs, microcontrollers) with strict constraints on latency and memory, limiting parameters to a few thousand. Conventional deep architectures are often impractical here. We propose the Fourier-Efficient Adaptive Temporal Hierarchy Forecaster (FEATHer) for accurate long-term forecasting under severe limits. FEATHer introduces: (i) ultra-lightweight multiscale decomposition into frequency pathways; (ii) a shared Dense Temporal Kernel using projection-depthwise convolution-projection without recurrence or attention; (iii) frequency-aware branch gating that adaptively fuses representations based on spectral characteristics; and (iv) a Sparse Period Kernel reconstructing outputs via period-wise downsampling to capture seasonality. FEATHer maintains a compact architecture (as few as 400 parameters) while outperforming baselines. Across eight benchmarks, it achieves the best ranking, recording 60 first-place results with an average rank of 2.05. These results demonstrate that reliable long-range forecasting is achievable on constrained edge hardware, offering a practical direction for industrial real-time inference.

翻译：时间序列预测在制造业和智能工厂等工业领域具有基础性地位。随着系统向自动化演进，模型必须在边缘设备（如可编程逻辑控制器、微控制器）上运行，这些设备对延迟和内存有严格限制，通常将参数量限制在数千以内。传统的深度架构在此类场景下往往不切实际。我们提出了傅里叶高效自适应时序层次预测器（FEATHer），用于在严苛限制下实现精确的长期预测。FEATHer引入了：（i）将信号超轻量级多尺度分解到频率通路中；（ii）使用无循环或注意力机制的投影-深度卷积-投影结构构建共享密集时序核；（iii）基于频谱特性自适应融合表征的频率感知分支门控机制；以及（iv）通过周期下采样重构输出以捕捉季节性的稀疏周期核。FEATHer保持了紧凑的架构（参数量可低至400个），同时性能优于基线模型。在八个基准测试中，它取得了最佳排名，共获得60项第一，平均排名为2.05。这些结果表明，在受限的边缘硬件上实现可靠的长期预测是可行的，为工业实时推理提供了一个实用的方向。