Abstract
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.
Abstract (translated)
时间序列预测在制造业和智能工厂等工业领域中至关重要。随着系统向自动化发展，模型必须能在边缘设备（如PLC、微控制器）上运行，并且这些设备对延迟和内存有严格的限制，这使得参数数量通常只能保持在几千以内。传统的深度架构在这种环境下往往不切实际。为此，我们提出了傅里叶高效自适应时间层次预报器（FEATHER），用于在极其有限的条件下进行准确的长期预测。 FEATHER引入了以下四个关键特性： (i) 极轻量级多尺度分解为频率路径； (ii) 一种共享的密集时间核使用投影-深度卷积-投影结构，不依赖递归或注意力机制； (iii) 频率感知分支门控，根据频谱特征自适应融合表示； (iv) 稀疏周期核通过按周期下采样重建输出以捕捉季节性模式。 FEATHER能够在保持紧凑架构（参数量低至400）的同时超越基准模型。在八个基准测试中，它获得了60项第一的成绩，并且平均排名为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实际的方向。
URL
https://arxiv.org/abs/2601.11350
