ts_fil_recursive {tspredit}

Recursive Filter

Description

Applies linear filtering to a univariate time series or to each series within a multivariate time series. It is useful for outlier detection, and the calculation is done recursively. This recursive calculation has the effect of reducing autocorrelation among observations, so that for each detected outlier, the filter is recalculated until there are no more outliers in the residuals.

Usage

ts_fil_recursive(filter)

Arguments

filter

smoothing parameter. The larger the value, the greater the smoothing. The smaller the value, the less smoothing, and the resulting series shape is more similar to the original series.

Value

a ts_fil_recursive object.

Examples

# time series with noise
library(daltoolbox)
data(sin_data)
sin_data$y[9] <- 2*sin_data$y[9]

# filter
filter <- ts_fil_recursive(filter =  0.05)
filter <- fit(filter, sin_data$y)
y <- transform(filter, sin_data$y)

# plot
plot_ts_pred(y=sin_data$y, yadj=y)