Mining time-frequency features is critical for time series forecasting. Existing research has predominantly focused on modeling low-frequency patterns, where most time series energy is concentrated. The overlooking of mid to high frequency continues to limit further performance gains in deep learning models. We propose FreqCycle, a novel framework integrating: (i) a Filter-Enhanced Cycle Forecasting (FECF) module to extract low-frequency features by explicitly learning shared periodic patterns in the time domain, and (ii) a Segmented Frequency-domain Pattern Learning (SFPL) module to enhance mid to high frequency energy proportion via learnable filters and adaptive weighting. Furthermore, time series data often exhibit coupled multi-periodicity, such as intertwined weekly and daily cycles. To address coupled multi-periodicity as well as long lookback window challenges, we extend FreqCycle hierarchically into MFreqCycle, which decouples nested periodic features through cross-scale interactions. Extensive experiments on seven diverse domain benchmarks demonstrate that FreqCycle achieves state-of-the-art accuracy while maintaining faster inference speeds, striking an optimal balance between performance and efficiency.

翻译：挖掘时频特征是时间序列预测的关键。现有研究主要集中于对低频模式进行建模，因为大部分时间序列能量集中于此。对中高频特征的忽视持续制约着深度学习模型性能的进一步提升。我们提出 FreqCycle，这是一个新颖的框架，整合了：(i) 一个滤波器增强的周期预测模块，通过在时域显式学习共享的周期模式来提取低频特征；以及 (ii) 一个分段频域模式学习模块，通过可学习滤波器和自适应加权来增强中高频能量占比。此外，时间序列数据常表现出耦合的多周期性，例如交织的周周期和日周期。为了解决耦合多周期性以及长回溯窗口的挑战，我们将 FreqCycle 层次化扩展为 MFreqCycle，该模型通过跨尺度交互解耦嵌套的周期特征。在七个不同领域的基准数据集上进行的大量实验表明，FreqCycle 在保持更快推理速度的同时，实现了最先进的预测精度，在性能与效率之间取得了最佳平衡。