Linear filtering on a time series

Applies linear filtering to a univariate "timeSeries".

Usage

# S4 method for class 'timeSeries'
filter(x, filter, method = c("convolution", "recursive"), sides = 2, 
       circular = FALSE, init = NULL)

Arguments

x

an object from class "timeSeries".

filter

coefficients of the filter.

method

"convolution" or "recursive".

sides,circular

for convolution filters only. Onesided if sides = 1, centred around lag 0 if sides = 2. Circular if circular = TRUE.

init

for recursive filters only. Values before the start of the time series.

Details

filter is a generic function with default method stats::filter. The method for "timeSeries" is a wrapper for the latter.

See ?stats::filter for details about the arguments.

Value

a "timeSeries" object

See also

base R function filter

Examples

## Creata a dummy signal 'timeSeries' - 
   data <- matrix(rnorm(100), ncol = 2)
   s <- timeSeries(data, units=c("A", "B"))
   head(s)
#>  
#>                 A          B
#> [1,]  0.706060014  0.4034617
#> [2,]  1.737579745  1.1048684
#> [3,]  0.877895632 -1.3096252
#> [4,]  0.006839866 -0.6550923
#> [5,] -1.439690971  0.9025630
#> [6,] -0.881909231 -0.5051588
   
## Filter the series - 
   f <- filter(s, rep(1, 3))
   head(f)
#>  
#>               A          B
#> [1,]         NA         NA
#> [2,]  3.3215354  0.1987049
#> [3,]  2.6223152 -0.8598490
#> [4,] -0.5549555 -1.0621545
#> [5,] -2.3147603 -0.2576880
#> [6,] -2.5417393  1.8277331
   
## Plot and compare the first series - 
   plot(cbind(s[, 1], f[, 1]), plot.type="s")