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