CpSdEwma function

Classic Processing Shift-Detection based on EWMA (SD-EWMA).

CpSdEwma calculates the anomalies of a dataset using classical processing based on the SD-EWMA algorithm. This algorithm is a novel method for covariate shift-detection tests based on a two-stage structure for univariate time-series. It works in an online mode and it uses an exponentially weighted moving average (EWMA) model based control chart to detect the covariate shift-point in non-stationary time-series. See also OcpSdEwma, the optimized and faster function of this function.

CpSdEwma(data, n.train, threshold = 0.01, l = 3)

Arguments

• data: Numerical vector with training and test dataset.
• n.train: Number of points of the dataset that correspond to the training set.
• threshold: Error smoothing constant.
• l: Control limit multiplier.

Returns

dataset conformed by the following columns:

• is.anomaly: 1 if the value is anomalous 0, otherwise.

• ucl: Upper control limit.

• lcl: Lower control limit.

Details

data must be a numerical vector without NA values. threshold must be a numeric value between 0 and 1. It is recommended to use low values such as 0.01 or 0.05. By default, 0.01 is used. Finally, l is the parameter that determines the control limits. By default, 3 is used.

Examples

## Generate data
set.seed(100)
n <- 180
x <- sample(1:100, n, replace = TRUE)
x[70:90] <- sample(110:115, 21, replace = TRUE)
x[25] <- 200
x[150] <- 170
df <- data.frame(timestamp = 1:n, value = x)

## Calculate anomalies
result <- CpSdEwma(
  data = df$value,
  n.train = 5,
  threshold = 0.01,
  l = 3
)
res <- cbind(df, result)

## Plot results
PlotDetections(res, title = "KNN-CAD ANOMALY DETECTOR")

References

Raza, H., Prasad, G., & Li, Y. (03 de 2015). EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments. Pattern Recognition, 48(3), 659-669.