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Open Access

QTFN: A General End-to-End Time-Frequency Network to Reveal the Time-Varying Signatures of the Time Series

State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
Shenzhen Key Laboratory of Intelligent Bioinformatics, and College of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
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Abstract

Nonstationary time series are ubiquitous in almost all natural and engineering systems. Capturing the time-varying signatures from nonstationary time series is still a challenging problem for data mining. Quadratic Time-Frequency Distribution (TFD) provides a powerful tool to analyze these data. However, they suffer from Cross-Term (CT) issues that impair the readability of TFDs. Therefore, to achieve high-resolution and CT-free TFDs, an end-to-end architecture termed Quadratic TF-Net (QTFN) is proposed in this paper. Guided by classic TFD theory, the design of this deep learning architecture is heuristic, which firstly generates various basis functions through data-driven. Thus, more comprehensive TF features can be extracted by these basis functions. Then, to balance the results of various basis functions adaptively, the Efficient Channel Attention (ECA) block is also embedded into QTFN. Moreover, a new structure called Muti-scale Residual Encoder-Decoder (MRED) is also proposed to improve the learning ability of the model by highly integrating the multi-scale learning and encoder-decoder architecture. Finally, although the model is only trained by synthetic signals, both synthetic and real-world signals are tested to validate the generalization capability and superiority of the proposed QTFN.

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Big Data Mining and Analytics
Pages 905-919
Cite this article:
Chen T, Jiao Y, Xie L, et al. QTFN: A General End-to-End Time-Frequency Network to Reveal the Time-Varying Signatures of the Time Series. Big Data Mining and Analytics, 2024, 7(3): 905-919. https://doi.org/10.26599/BDMA.2024.9020031

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Received: 24 October 2023
Revised: 07 May 2024
Accepted: 09 May 2024
Published: 28 August 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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