Signal Decomposition

3min

The Signal Decomposition processor separates signal components from composite signals related to semantic units. Examples for this are distinct objects in images or video, video shots, melody sequences in music, spoken words or sentences in speech signals.
Signal Decomposition Overview
Native version of signal decomposition.
Additive model: Signal = Trend + Seasonality + Residue
Multiplicative model: Signal = Trend * Seasonality * Residue
Trend is calculated by linear regression.
Seasonality is calculated by naive differencing.
Signal Decomposition Parameters
Parameters
Details
Window Size
This parameter specifies how many values are included in the periodic interval for the signal decomposition process.


Model Type
This parameter defines the type of model to be used for the signal decomposition.
Periodicity
This parameter determines the periodic interval for the signal decomposition, providing the frequency at which the seasonal components are expected to repeat in the data.
Signal Decomposition parameters
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Note: When creating an analytics flow with Signal Decomposition processor, refer the Use the Signal Decomposition Function﻿  guide for more details.
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Parameters	Details
Window Size	This parameter specifies how many values are included in the periodic interval for the signal decomposition process.
Model Type	This parameter defines the type of model to be used for the signal decomposition.
Periodicity	This parameter determines the periodic interval for the signal decomposition, providing the frequency at which the seasonal components are expected to repeat in the data.

Updated 23 Sep 2024

Rounding

Simple Window