Time-series forecasting is crucial for numerous real-world applications including weather prediction and financial market modeling. While temporal-domain methods remain prevalent, frequency-domain approaches can effectively capture multi-scale periodic patterns, reduce sequence dependencies, and naturally denoise signals. However, existing approaches typically train model components for all frequencies under a unified training objective, often leading to mismatched learning speeds: high-frequency components converge faster and risk overfitting, while low-frequency components underfit due to insufficient training time. To deal with this challenge, we propose BEAT (Balanced frEquency Adaptive Tuning), a novel framework that dynamically monitors the training status for each frequency and adaptively adjusts their gradient updates. By recognizing convergence, overfitting, or underfitting for each frequency, BEAT dynamically reallocates learning priorities, moderating gradients for rapid learners and increasing those for slower ones, alleviating the tension between competing objectives across frequencies and synchronizing the overall learning process. Extensive experiments on seven real-world datasets demonstrate that BEAT consistently outperforms state-of-the-art approaches.

翻译：时间序列预测对于天气预测和金融市场建模等众多现实应用至关重要。虽然时域方法仍占主导地位，但频域方法能有效捕捉多尺度周期性模式、降低序列依赖性并自然地对信号进行去噪。然而，现有方法通常在统一的训练目标下为所有频率训练模型组件，这常常导致学习速度不匹配：高频分量收敛更快且存在过拟合风险，而低频分量则因训练时间不足而欠拟合。为应对这一挑战，我们提出了BEAT（平衡频率自适应调优），这是一个新颖的框架，能够动态监控每个频率的训练状态并自适应地调整其梯度更新。通过识别每个频率的收敛、过拟合或欠拟合情况，BEAT动态地重新分配学习优先级，减缓快速学习者的梯度并增加慢速学习者的梯度，从而缓解不同频率间竞争目标的张力并同步整体学习过程。在七个真实世界数据集上的大量实验表明，BEAT始终优于最先进的方法。