We introduce a model-agnostic forward diffusion process for time-series forecasting that decomposes signals into spectral components, preserving structured temporal patterns such as seasonality more effectively than standard diffusion. Unlike prior work that modifies the network architecture or diffuses directly in the frequency domain, our proposed method alters only the diffusion process itself, making it compatible with existing diffusion backbones (e.g., DiffWave, TimeGrad, CSDI). By staging noise injection according to component energy, it maintains high signal-to-noise ratios for dominant frequencies throughout the diffusion trajectory, thereby improving the recoverability of long-term patterns. This strategy enables the model to maintain the signal structure for a longer period in the forward process, leading to improved forecast quality. Across standard forecasting benchmarks, we show that applying spectral decomposition strategies, such as the Fourier or Wavelet transform, consistently improves upon diffusion models using the baseline forward process, with negligible computational overhead. The code for this paper is available at https://anonymous.4open.science/r/D-FDP-4A29.

翻译：本文提出了一种与模型无关的时间序列预测前向扩散过程，该方法将信号分解为频谱分量，比标准扩散方法更有效地保留季节等结构化时间模式。与以往通过修改网络架构或在频域直接进行扩散的研究不同，我们提出的方法仅改变扩散过程本身，使其与现有的扩散主干网络（如 DiffWave、TimeGrad、CSDI）兼容。通过根据分量能量分阶段注入噪声，该方法在整个扩散轨迹中为主频分量保持高信噪比，从而提高了长期模式的可恢复性。这一策略使得模型在前向过程中能更长时间地保持信号结构，从而提升了预测质量。在标准预测基准测试中，应用频谱分解策略（如傅里叶变换或小波变换）相比使用基线前向过程的扩散模型，均能带来一致的性能提升，且计算开销可忽略不计。本文代码可在 https://anonymous.4open.science/r/D-FDP-4A29 获取。