# Documentation

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# Moving Variance

Moving variance

• Library:
• DSP System Toolbox / Statistics

## Description

The Moving Variance block computes the moving variance of the input signal along each channel independently over time. The block uses either the sliding window method or the exponential weighting method to compute the moving variance. In the sliding window method, a window of specified length moves over the data sample by sample, and the block computes the variance over the data in the window. In the exponential weighting method, the block subtracts each sample of the data from the average, squares the difference, and multiplies the squared result by a weighting factor. The object then computes the variance by adding all the weighted data. For more details on these methods, see Algorithms.

## Ports

### Input

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Data over which the block computes the moving variance. The block accepts real-valued or complex-valued multichannel inputs, that is, m-by-n size inputs, where m ≥ 1, and n ≥ 1. The block also accepts variable-size inputs. During simulation, you can change the size of each input channel. However, the number of channels cannot change.

Data Types: single | double
Complex Number Support: Yes

### Output

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The size of the moving variance output matches the size of the input. The block uses either the sliding window method or the exponential weighting method to compute the moving variance. For more details, see Algorithms.

Data Types: single | double
Complex Number Support: Yes

## Parameters

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• Sliding window — A window of length Window length moves over the input data along each channel. For every sample the window moves by, the block computes the variance over the data in the window.

• Exponential weighting — The block subtracts each sample of the data from the average, squares the difference, and multiplies the squared result by a weighting factor. The block then computes the variance by adding all the weighted data. The magnitude of the weighting factors decreases exponentially as the age of the data increases, never reaching zero.

For more details on these methods, see Algorithms.

When you select this check box, the length of the sliding window is equal to the value you specify in . When you clear this check box, the length of the sliding window is infinite. In this mode, the block computes the variance of the current sample with respect to all the previous samples in the channel.

Window length specifies the length of the sliding window. This parameter appears when you select the check box.

This parameter applies when you set Method to Exponential weighting. A forgetting factor of 0.9 gives more weight to the older data than does a forgetting factor of 0.1. A forgetting factor of 1.0 indicates infinite memory. All the previous samples are given an equal weight.

This parameter is tunable. You can change its value even during the simulation.

• Code generation

Simulate model using generated C code. The first time you run a simulation, Simulink® generates C code for the block. The C code is reused for subsequent simulations, as long as the model does not change. This option requires additional startup time but provides faster simulation speed than Interpreted execution.

• Interpreted execution

Simulate model using the MATLAB®  interpreter. This option shortens startup time but has slower simulation speed than Code generation.

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