Fractional fourier transform in time series prediction
Date
2022-12-09Source Title
IEEE Signal Processing Letters
Print ISSN
1070-9908
Electronic ISSN
1558-2361
Publisher
IEEE
Volume
29
Pages
2542 - 2546
Language
English
Type
ArticleItem Usage Stats
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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 analysisFeature extraction
Decoding
Fourier transforms
Training
Wavelet transforms
Recurrent neural networks