otsad (version 0.2.0)

IpKnnCad: Incremental processing KNN based Conformal Anomaly Detector (KNN-CAD).

Description

IpKnnCad allows the calculation of anomalies using SD-EWMA in an incremental processing mode. KNN-CAD is a model-free anomaly detection method for univariate time-series which adapts itself to non-stationarity in the data stream and provides probabilistic abnormality scores based on the conformal prediction paradigm.

Usage

IpKnnCad(data, n.train, threshold = 1, l = 19, k = 27,
  ncm.type = "ICAD", reducefp = TRUE, to.next.iteration = NULL)

Arguments

data

Numerical vector with training and test dataset.

n.train

Number of points of the dataset that correspond to the training set.

threshold

Anomaly threshold.

l

Window length.

k

Number of neighbours to take into account.

ncm.type

Non Conformity Measure to use "ICAD" or "LDCD"

reducefp

If TRUE reduces false positives.

to.next.iteration

list with the necessary parameters to execute in the next iteration.

Value

dataset conformed by the following columns:

is.anomaly

1 if the value is anomalous 0, otherwise.

anomaly.score

Probability of anomaly.

to.next.iteration

Last result returned by the algorithm. It is a list containing the following items.

training.set Last training set values used in the previous iteration and required for the next run. calibration.set Last calibration set values used in the previous iteration and required for the next run. sigma Last covariance matrix calculated in the previous iteration and required for the next run. alphas Last calibration alpha values calculated in the previous iteration and required for the next run. last.data Last values of the dataset converted into multi-dimensional vectors.. pred Parameter that is used to reduce false positives. Only necessary in case of reducefp is TRUE. record.count Number of observations that have been processed up to the last iteration.

Details

data must be a numerical vector without NA values. threshold must be a numeric value between 0 and 1. If the anomaly score obtained for an observation is greater than the threshold, the observation will be considered abnormal. l must be a numerical value between 1 and 1/n; n being the length of the training data. Take into account that the value of l has a direct impact on the computational cost, so very high values will make the execution time longer. k parameter must be a numerical value less than the n.train value. ncm.type determines the non-conformity measurement to be used. ICAD calculates dissimilarity as the sum of the distances of the nearest k neighbours and LDCD as the average. to.next.iteration is the last result returned by some previous execution of this algorithm. The first time the algorithm is executed its value is NULL. However, to run a new batch of data without having to include it in the old dataset and restart the process, this parameter returned by the last run is only needed.

This algorithm can be used for both classical and incremental processing. It should be noted that in case of having a finite dataset, the CpKnnCad algorithm is faster. Incremental processing can be used in two ways. 1) Processing all available data and saving calibration.alpha and last.data for future runs with new data. 2) Using the stream library for when there is much data and it does not fit into the memory. An example has been made for this use case.

References

V. Ishimtsev, I. Nazarov, A. Bernstein and E. Burnaev. Conformal k-NN Anomaly Detector for Univariate Data Streams. ArXiv e-prints, jun. 2017.

Examples

# NOT RUN {
## EXAMPLE 1: ----------------------
## It can be used in the same way as with CpKnnCad passing the whole dataset as
## an argument.

## Generate data
set.seed(100)
n <- 500
x <- sample(1:100, n, replace = TRUE)
x[70:90] <- sample(110:115, 21, replace = TRUE)
x[25] <- 200
x[320] <- 170
df <- data.frame(timestamp = 1:n, value = x)

## Set parameters
params.KNN <- list(threshold = 1, n.train = 50, l = 19, k = 17)

## Calculate anomalies
result <- IpKnnCad(
  data = df$value,
  n.train = params.KNN$n.train,
  threshold = params.KNN$threshold,
  l = params.KNN$l,
  k = params.KNN$k,
  ncm.type = "ICAD",
  reducefp = TRUE
)

## Plot results
res <- cbind(df, is.anomaly = result$is.anomaly)
PlotDetections(res, print.time.window = FALSE, title = "KNN-CAD ANOMALY DETECTOR")

## EXAMPLE 2: ----------------------
## You can use it in an incremental way. This is an example using the stream
## library. This library allows the simulation of streaming operation.
# }
# NOT RUN {
# install.packages("stream")
library("stream")

## Generate data
set.seed(100)
n <- 500
x <- sample(1:100, n, replace = TRUE)
x[70:90] <- sample(110:115, 21, replace = TRUE)
x[25] <- 200
x[320] <- 170
df <- data.frame(timestamp = 1:n, value = x)
dsd_df <- DSD_Memory(df)

## Initialize parameters for the loop
last.res <- NULL
res <- NULL
nread <- 100
numIter <- n%/%nread

## Set parameters
params.KNN <- list(threshold = 1, n.train = 50, l = 19, k = 17)

## Calculate anomalies
for(i in 1:numIter) {
  # read new data
  newRow <- get_points(dsd_df, n = nread, outofpoints = "ignore")
  # calculate if it's an anomaly
  last.res <- IpKnnCad(
    data = newRow$value,
    n.train = params.KNN$n.train,
    threshold = params.KNN$threshold,
    l = params.KNN$l,
    k = params.KNN$k,
    ncm.type = "ICAD",
    reducefp = TRUE,
    to.next.iteration = last.res$to.next.iteration
  )
  # prepare the result
  if(!is.null(last.res$is.anomaly)){
    res <- rbind(res, cbind(newRow, is.anomaly = last.res$is.anomaly))
  }
}

## Plot results
PlotDetections(res, title = "KNN-CAD ANOMALY DETECTOR")
# }
# NOT RUN {

# }