Fractional fourier transform in time series prediction

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buir.contributor.authorKoç, Emirhan
buir.contributor.authorKoç, Koç, Aykut
buir.contributor.orcidKoç, Emirhan|0000-0002-7275-1570
buir.contributor.orcidKoç, Aykut|0000-0002-6348-2663
dc.citation.epage2546en_US
dc.citation.spage2542en_US
dc.citation.volumeNumber29en_US
dc.contributor.authorKoç, Emirhan
dc.contributor.authorKoç, Aykut
dc.date.accessioned2023-02-13T11:41:24Z
dc.date.available2023-02-13T11:41:24Z
dc.date.issued2022-12-09
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractSeveral 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.en_US
dc.identifier.doi10.1109/LSP.2022.3228131en_US
dc.identifier.eissn1558-2361
dc.identifier.issn1070-9908
dc.identifier.urihttp://hdl.handle.net/11693/111214
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttps://www.doi.org/10.1109/LSP.2022.3228131en_US
dc.source.titleIEEE Signal Processing Lettersen_US
dc.subjectTime series analysisen_US
dc.subjectFeature extractionen_US
dc.subjectDecodingen_US
dc.subjectFourier transformsen_US
dc.subjectTrainingen_US
dc.subjectWavelet transformsen_US
dc.subjectRecurrent neural networksen_US
dc.titleFractional fourier transform in time series predictionen_US
dc.typeArticleen_US

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