FEATHer: Fourier-Efficient Adaptive Temporal Hierarchy Forecaster for Time-Series Forecasting

TL;DR

FEATHer is a lightweight, Fourier-based time-series forecasting model designed for edge devices, achieving high accuracy with minimal parameters and outperforming existing methods across multiple benchmarks.

Contribution

The paper introduces FEATHer, a novel ultra-lightweight Fourier-based architecture for long-term forecasting suitable for resource-constrained edge devices.

Findings

Achieves state-of-the-art results on eight benchmarks.

Maintains high accuracy with as few as 400 parameters.

Outperforms baselines in industrial time-series forecasting tasks.

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.