1.1 - 8.10 - DWT (ML Engine) - Teradata Vantage

Teradata Vantage™ - Machine Learning Engine Analytic Function Reference

Teradata Vantage
Release Number
October 2019
Content Type
Programming Reference
Publication ID
English (United States)

The DWT function implements the Mallat algorithm (an iterate algorithm in the Discrete Wavelet Transform field) and applies wavelet transform on multiple sequences simultaneously.

The input is typically a set of time series sequences. You specify the wavelet name or wavelet filter table, transform level, and (optionally) extension mode. The function returns the transformed sequences in Hilbert space with the corresponding component identifiers and indices. (The transformation is also called the decomposition.)

How Machine Learning Engine function DWT works

The wavelet filter table does not appear in the preceding diagram because it is seldom used.

You can filter the result to reduce the lengths of the transformed sequences and then use the function IDWT (ML Engine) to reconstruct them; therefore, the DWT and IDWT functions are useful for compression and removing noise.