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      • Department of Electrical and Electronics Engineering
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      Fractional fourier transform in time series prediction

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      Author(s)
      Koç, Emirhan
      Koç, Aykut
      Date
      2022-12-09
      Source Title
      IEEE Signal Processing Letters
      Print ISSN
      1070-9908
      Electronic ISSN
      1558-2361
      Publisher
      IEEE
      Volume
      29
      Pages
      2542 - 2546
      Language
      English
      Type
      Article
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      Abstract
      Several signal processing tools are integrated into machine learning models for performance and computational cost improvements. Fourier transform (FT) and its variants, which are powerful tools for spectral analysis, are employed in the prediction of univariate time series by converting them to sequences in the spectral domain to be processed further by recurrent neural networks (RNNs). This approach increases the prediction performance and reduces training time compared to conventional methods. In this letter, we introduce fractional Fourier transform (FrFT) to time series prediction by RNNs. As a parametric transformation, FrFT allows us to seek and select better-performing transformation domains by providing access to a continuum of domains between time and frequency. This flexibility yields significant improvements in the prediction power of the underlying models without sacrificing computational efficiency. We evaluated our FrFT-based time series prediction approach on synthetic and real-world datasets. Our results show that FrFT gives rise to performance improvements over ordinary FT.
      Keywords
      Time series analysis
      Feature extraction
      Decoding
      Fourier transforms
      Training
      Wavelet transforms
      Recurrent neural networks
      Permalink
      http://hdl.handle.net/11693/111214
      Published Version (Please cite this version)
      https://www.doi.org/10.1109/LSP.2022.3228131
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      • Department of Electrical and Electronics Engineering 4011
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