otsad (version 0.2.0)

NormalizeScore: Normalize Score using Max and Min normalization

Description

ReduceAnomalies It reduces the number of detected anomalies. This function is designed to reduce the number of false positives keeping only the first detection of all those that are close to each other. This proximity distance is defined by a window

Usage

NormalizeScore(real.score, perfect.score, null.score)

Arguments

real.score

Detector score. See GetDetectorScore.

perfect.score

Perfect detector score; one that outputs all true positives and no false positives. See GetNullAndPerfectScores.

null.score

Perfect detector score; one that outputs all true positives and no false positives. See GetNullAndPerfectScores.

Value

Normalized score.

References

A. Lavin and S. Ahmad, <U+201C>Evaluating Real-time Anomaly Detection Algorithms <U+2013> the Numenta Anomaly Benchmark,<U+201D> in 14th International Conference on Machine Learning and Applications (IEEE ICMLA<U+2019>15), 2015.

Examples

# NOT RUN {
## 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)

# Add is.real.anomaly column
df$is.real.anomaly <- 0
df[c(25,80,150), "is.real.anomaly"] <- 1

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

# Get null and perfect scores
np.scores <- GetNullAndPerfectScores(df)
np.standard <- np.scores[1,]
np.fp <- np.scores[2,]
np.fn <- np.scores[3,]

# Get detector score
scores <- GetDetectorScore(res, print = FALSE, title = "")

# Normalize standard score
NormalizeScore(scores$standard, np.standard$perfect.score, np.standard$null.score)

# Normalize low_FP_rate score
NormalizeScore(scores$low_FP_rate, np.fp$perfect.score, np.fp$null.score)

# Normalize low_FN_rate score
NormalizeScore(scores$low_FN_rate, np.fn$perfect.score, np.fn$null.score)
# }