frequency-analysis

ORBIT 提出了一种统一的深度时间序列生成框架，通过频率层次化 VQ-VAE 和频率带自回归 Transformer 实现从低频到高频的下一频带预测，从而先构建全局时间结构再逐步补充细节，在多个真实基准上达到最先进的性能与更高的推理效率。

SALD 提出了一种自参考训练框架，通过在两个噪声水平下用同一模型评估图像-文本对来获取训练信号，无需外部教师网络或额外可训练参数，包含优势引导扩散（AGD）、时间优势记忆（TAM）和谱优势分解（SAD）三个组件，在多个架构和数据集上持续提升了生成质量。

The paper proposes FreqDiff, a frequency-aware diffusion framework for temporal knowledge graph extrapolation that improves uncertainty modeling and achieves state-of-the-art performance on benchmarks.

RouteTS is a unified forecasting framework that routes time series components between frequency and time domains based on spectral characteristics, improving accuracy and efficiency in handling periodicity and transience.

This paper introduces the Spectral Energy Centroid (SEC) metric to analyze and improve spectral bias in implicit neural representations, demonstrating its utility for hyperparameter selection, signal complexity measurement, and cross-architecture alignment.