outDetect: Detect outliers in a time series considering the raw data and...

In INLAspacetime: Spatial and Spatio-Temporal Models using 'INLA'

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

Description

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))

Arguments

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.

Value

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

• attr(, 'xs') the smoothed time series y_t

INLAspacetime documentation built on April 4, 2025, 1:38 a.m.