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| Titel |
Discrete Fourier Transform: statistical effect size and significance of Fourier components. |
| VerfasserIn |
Robin Crockett |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250127182
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| Publikation (Nr.) |
 EGU/EGU2016-7026.pdf |
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| Zusammenfassung |
| A key analytical technique in the context of investigating cyclic/periodic features in time-series (and other sequential data) is the Discrete (Fast) Fourier Transform (DFT/FFT). However, assessment of the statistical effect-size and significance of the Fourier components in the DFT/FFT spectrum can be subjective and variable. This presentation will outline an approach and method for the statistical evaluation of the effect-size and significance of individual Fourier components from their DFT/FFT coefficients. The effect size is determined in terms of the proportions of the variance in the time-series that individual components account for. The statistical significance is determined using an hypothesis-test / p-value approach with respect to a null hypothesis that the time-series has no linear dependence on a given frequency (of a Fourier component). This approach also allows spectrograms to be presented in terms of these statistical parameters. The presentation will use sunspot cycles as an illustrative example. |
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