TestPointIsAnomaly: Check Whether Test Point is Anomaly

In jingjin1018/anetimeseries: An Implementation of Functions Used For Time Series Analysis Within A&E

Description

Assume the training set was generated by a process that follows certain distribution, i.e. "normal" or "t", this function checks whether to reject the null hypothesis that the test point are generated by the same process. If the probability of obtaining a result equals to or more extreme than test is lower than p, then this function returns TRUE, meaning the null hypothesis is rejected and the test point is likely to be an anomoly. If argument exclude is specified, elements at those designated positions are removed from the training set.

Usage

TestPointIsAnomaly(training, test, dist = "t", exclude = NULL, p = 0.01,
  direction = direction, ...)

Arguments

training	A numeric vector containing the samples used to fit the distribution
test	A numeric value to be tested
dist	A string specifies the distribution to fit. Options are t-distribution(default: dist = "t"), normal distribution(dist = "normal").
exclude	A logical vector with length equals to length(training). It is used to remove elements at designated positions from fitting the distribution. By default, exclude = NULL, which means no element is excluded.
p	p-value threshold with values in [0, 1].
direction	Directionality of the anomalies to be deteted. Options are: 'pos', 'neg' and 'both'. Defaults to be 'both'.

Value

returns TRUE if the test point is likely to be an anomaly and FALSE otherwise. For debugging purpose, this function also returns metadata median, mad, score and hist.

• median sample median of training set

• mad sample mean absolute deviation calculated from training set

• score defined as (test - median) / mad

• hist histogram of training points overlaid with stats function used to fit

Examples

set.seed(1)
training <- rt(1000, df = 10)
test <- 4
exclude <- sample(c(T, F), 1000, replace = T, prob = c(0.005, 0.995))
r <- TestPointIsAnomaly(training, test, dist = "t", exclude = exclude, p = 0.1, df = 10)
attr(r, "hist")
training <- rnorm(1000, mean = 0, sd = 10)
r <- TestPointIsAnomaly(training, test, dist = "normal", p = 0.1)
attr(r, "hist")

jingjin1018/anetimeseries documentation built on May 19, 2019, 10:35 a.m.