filter {stats} | R Documentation |

## Linear Filtering on a Time Series

### Description

Applies linear filtering to a univariate time series or to each series separately of a multivariate time series.

### Usage

```
filter(x, filter, method = c("convolution", "recursive"),
sides = 2, circular = FALSE, init)
```

### Arguments

`x` |
a univariate or multivariate time series. |

`filter` |
a vector of filter coefficients in reverse time order (as for AR or MA coefficients). |

`method` |
Either |

`sides` |
for convolution filters only. If |

`circular` |
for convolution filters only. If |

`init` |
for recursive filters only. Specifies the initial values of the time series just prior to the start value, in reverse time order. The default is a set of zeros. |

### Details

Missing values are allowed in `x`

but not in `filter`

(where they would lead to missing values everywhere in the output).

Note that there is an implied coefficient 1 at lag 0 in the recursive filter, which gives

`y_i = x_i + f_1y_{i-1} + \cdots + f_py_{i-p}`

No check is made to see if recursive filter is invertible: the output may diverge if it is not.

The convolution filter is

`y_i = f_1x_{i+o} + \cdots + f_px_{i+o-(p-1)}`

where `o`

is the offset: see `sides`

for how it is determined.

### Value

A time series object.

### Note

`convolve(, type = "filter")`

uses the FFT for computations
and so *may* be faster for long filters on univariate series,
but it does not return a time series (and so the time alignment is
unclear), nor does it handle missing values. `filter`

is
faster for a filter of length 100 on a series of length 1000,
for example.

### See Also

### Examples

```
x <- 1:100
filter(x, rep(1, 3))
filter(x, rep(1, 3), sides = 1)
filter(x, rep(1, 3), sides = 1, circular = TRUE)
filter(presidents, rep(1, 3))
```

*stats*version 4.4.1 Index]