Recently, frequency transformation (FT) has been increasingly incorporated into deep learning models to significantly enhance state-of-the-art accuracy and efficiency in time series analysis. The advantages of FT, such as high efficiency and a global view, have been rapidly explored and exploited in various time series tasks and applications, demonstrating the promising potential of FT as a new deep learning paradigm for time series analysis. Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT. It is also unclear why FT can enhance time series analysis and what its limitations in the field are. To address these gaps, we present a comprehensive review that systematically investigates and summarizes the recent research advancements in deep learning-based time series analysis with FT. Specifically, we explore the primary approaches used in current models that incorporate FT, the types of neural networks that leverage FT, and the representative FT-equipped models in deep time series analysis. We propose a novel taxonomy to categorize the existing methods in this field, providing a structured overview of the diverse approaches employed in incorporating FT into deep learning models for time series analysis. Finally, we highlight the advantages and limitations of FT for time series modeling and identify potential future research directions that can further contribute to the community of time series analysis.

翻译：近年来，频率变换（FT）被越来越多地融入深度学习模型，显著提升了时间序列分析中最先进的准确性和效率。FT具有高效率和全局视野等优势，已在各类时间序列任务与应用中得到快速探索和利用，展现出其作为时间序列分析领域新型深度学习范式的巨大潜力。尽管这一新兴领域备受关注且研究日益丰富，但目前仍缺乏对基于频率变换的深度学习时间序列模型的系统综述与深入分析。同时，FT为何能增强时间序列分析效果、其在该领域存在哪些局限性也不甚明晰。为填补上述空白，本文提出一项全面综述，系统梳理和总结近年来基于频率变换的深度学习时间序列分析研究进展。具体而言，我们探讨了当前融入FT的模型所采用的主要方法、利用FT的神经网络类型，以及深度时间序列分析中具有代表性的FT增强模型。我们提出了一种新颖的分类体系来归纳该领域的现有方法，为将FT融入深度学习模型进行时间序列分析的各种途径提供了结构化概述。最后，我们着重阐述了FT在时间序列建模中的优势与局限性，并指明了能够进一步推动时间序列分析社区发展的潜在未来研究方向。