Documentation

# filter

Class: LagOp

Apply lag operator polynomial to filter time series

## Syntax

```[Y,times] = filter(A,X) [Y,times] = filter(A,X,'Initial',X0) ```

## Description

Given a lag operator polynomial A(L), ```[Y,times] = filter(A,X)```applies A(L) to time series data X(t). This is equivalent to applying a linear filter to X(t), producing the filtered output series Y(t) = A(L)X(t).

```[Y,times] = filter(A,X,'Initial',X0)``` applies A(L) to time series data X(t) with specified presample values of the input time series X(t).

## Input Arguments

 `A` Lag operator polynomial object, as produced by `LagOp`. `X` `numObs`-by-`numDims` matrix of time series data to which the lag operator polynomial `A` is applied. The last observation is assumed to be the most recent. `numDims` is the dimension of `A`, unless `X` is a row vector, in which case `X` is treated as a univariate series. For univariate `X`, the orientation of the output `Y` is determined by the orientation of the input X. `'Initial'` Presample values of the input time series X(t). If `'Initial'` is unspecified, or if the number of presample values is insufficient to initialize filtering, values are taken from the beginning of `X`, reducing the effective sample size of the output `Y`. For convenience, scalar presample values are expanded to provide all `numPresampleObs`-by-`numDims` presample values, and data is not taken from `X`. If more presample values are specified than necessary, only the most recent values are used. For univariate `X`, presample values can be a row or a column vector.

## Output Arguments

 `Y` Filtered input time series, Y(t) = A(L)X(t). `times` Vector of relative time indices the same length as `Y`. Times are expressed relative to, or as an offset from, observations times 0, 1, 2,...,`numObs`–1 for the input series X(t). For a polynomial of degree p, Y(0) is a linear combination of X(t) for times t = 0, –1, –2,...,–p (presample data). Y(t) for t > 0 is a linear combination of X(t) for times t = t, t–1, t–2,...,t–p.

## Examples

expand all

Create a `LagOp` polynomial and a random time series:

```rng('default') % Make output reproducible A = LagOp({1 -0.6 0.08 0.2}, 'Lags', [0 1 2 4]); X = randn(10, A.Dimension);```

Filter the input time series with no explicit initial observations, allowing the `filter` method to automatically strip all required initial data from the beginning of the input time series $X\left(t\right)$.

`[Y1,T1] = filter(A, X);`

Manually strip all required presample observations directly from the beginning of $X\left(t\right)$, then pass in the reduced-length $X\left(t\right)$ and the stripped presample observations directly to the `filter` method. In this case, the first 4 observations of $X\left(t\right)$ are stripped because the degree of the lag operator polynomial created below is 4.

```[Y2,T2] = filter(A, X((A.Degree + 1):end,:), ... 'Initial', X(1:A.Degree,:));```

Manually strip part of the required presample observations from the beginning of $X\left(t\right)$ and let the `filter` method automatically strip the remaining observations from $X\left(t\right)$.

```[Y3,T3] = filter(A, X((A.Degree - 1):end,:), ... 'Initial', X(1:A.Degree - 2,:));```

The filtered output series are all the same. However, the associated time vectors are not.

`disp([T1 T2 T3])`
``` 4 0 2 5 1 3 6 2 4 7 3 5 8 4 6 9 5 7 ```

## Algorithms

Filtering is limited to single paths, so matrix data are assumed to be a single path of a multidimensional process, and 3-D data (multiple paths of a multidimensional process) are not allowed.