Fast Fourier Transform Functions - Teradata Vantage

Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
8.00
1.0
Published
May 2019
Language
English (United States)
Last Update
2019-11-22
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B700-4003
lifecycle
previous
Product Category
Teradata Vantageā„¢

Fast Fourier Transform (FFT), developed by Cooley and Tukey in 1965, is an algorithm that computes the discrete Fourier Transform (DFT) of a signal. FFT significantly reduces the complexity of the Fourier Transform algorithm by exploiting the symmetry and periodicity of a Fourier Transform and using a divide-and-conquer strategy.

The divide-and conquer-strategy that the ML Engine FFT function uses is Radix-2, Radix-4, or Radix-8, for a signal whose length is a power of 2, 4, or 8, respectively.

Function Description
FFT Uses FFT algorithm to compute DFT of each signal in one or more input table columns.
IFFT Uses inverse Fast Fourier Transform (IFFT) algorithm (also called a Fourier synthesis algorithm) to reverse Fast Fourier Transform performed by FFT function; that is, the IFFT function takes a frequency domain representation and combines the contributions of all the different frequencies to recover the original signal.