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# filter

Modify frequency content of timeseries objects

## Syntax

tsout = filter(tsin,b,a)
tsout = filter(tsin,b,a,ind)

## Description

tsout = filter(tsin,b,a) applies the rational transfer function filter b(z−1)/a(z−1) to the uniformly-spaced data in the timeseries object tsin. The numerator b and denominator a are vectors containing the transfer function coefficients.

tsout = filter(tsin,b,a,ind) specifies the indices of the columns or rows to filter. ind is a vector of integers representing column indices for column-oriented data (tsin.IsTimeFirst is true) and representing row indices for row-oriented data (tsin.IsTimeFirst is false).

## Examples

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This example applies the following transfer function to a set of data:

Create a timeseries object from the matrix count in count.dat.

tsin = timeseries(count(:,1),[1:24]);

Enter the coefficients for the denominator and numerator of the transfer function. Order the coefficients in ascending powers of to represent and .

a = [1 0.2];
b = [2 3];

Apply the transfer function using filter, and compare the original data to the filtered data.

tsout = filter(tsin,b,a);
plot(tsin)
hold on
plot(tsout)
legend('Original Data','Filtered Data','Location','NorthWest')

## Input Arguments

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Input timeseries, specified as a scalar. tsin must be uniformly sampled.

Data Types: timeseries

Numerator coefficients of the transfer function, specified as a scalar or vector.

Denominator coefficients of the transfer function, specified as a scalar or vector.

Row or column indices, specified as a positive integer numeric scalar or vector. ind represents column indices for column-oriented data (tsin.IsTimeFirst is true) and represents row indices for row-oriented data (tsin.IsTimeFirst is false).

Data Types: double | single | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

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### Rational Transfer Function

The input-output description of the filter operation on a vector in the Z-transform domain is a rational transfer function. A rational transfer function is of the form,

$Y\left(z\right)=\frac{b\left(1\right)+b\left(2\right){z}^{-1}+...+b\left({n}_{b}+1\right){z}^{-{n}_{b}}}{1+a\left(2\right){z}^{-1}+...+a\left({n}_{a}+1\right){z}^{-{n}_{a}}}X\left(z\right),$

which handles both FIR and IIR filters [1]. na is the feedback filter order, and nb is the feedforward filter order.

You also can express the rational transfer function as the following difference equation,

$\begin{array}{c}a\left(1\right)y\left(n\right)=b\left(1\right)x\left(n\right)+b\left(2\right)x\left(n-1\right)+...+b\left({n}_{b}+1\right)x\left(n-{n}_{b}\right)\\ -a\left(2\right)y\left(n-1\right)-...-a\left({n}_{a}+1\right)y\left(n-{n}_{a}\right).\end{array}$

Furthermore, you can represent the rational transfer function using its direct form II transposed implementation, as in the following diagram. Due to normalization, assume a(1) = 1.

The operation of filter at sample m is given by the time domain difference equations