Detect outliers in a time series considering the raw data and a smoothed version of it.

Usage

outDetect(x, weights = NULL, ff = c(7, 7))

x: numeric vector
weights: non-increasing numeric vector used as weights for computing a smoothed vector as a rooling window average. Default is null and then \(w_j\) is proportional to j in the equation in the Details below.
ff: numeric length two vector with the factors used to consider how many times the standard deviation one data point is out to be considered as an outlier.

logical vector indicating if the data is an outlier with attributes as detailed bellow.

attr(, 'm') is the mean of x.
attr(, 's') is the standard devation of x.
attr(, 'ss') is the standard deviation for the smoothed data \(y_t\) that is defined as

\(y_t = \sum_{k=j}^h w_j * (x_{t-j}+x_{t+j})/2\)

Both s and ss are used to define outliers if

\(|x_t-m|/s>ff_1\) or \(|x_t-y_t|/ss>ff_2\)