A Survey on Deep Learning based Time Series Analysis with Frequency Transformation

• URL: http://arxiv.org/abs/2302.02173v4
• Date: Sun, 15 Oct 2023 07:45:40 GMT
• Title: A Survey on Deep Learning based Time Series Analysis with Frequency Transformation
• Authors: Kun Yi and Qi Zhang and Longbing Cao and Shoujin Wang and Guodong Long and Liang Hu and Hui He and Zhendong Niu and Wei Fan and Hui Xiong
• Abstract summary: Frequency transformation (FT) has been increasingly incorporated into deep learning models to enhance state-of-the-art accuracy and efficiency in 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. We present a comprehensive review that systematically investigates and summarizes the recent research advancements in deep learning-based time series analysis with FT.
• Score: 74.3919960186696
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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