Anomaly detector using histogram

Usage

hanr_histogram(density_threshold = 0.05)

Arguments

density_threshold: It is the minimum frequency for a bin to not be considered an anomaly. Default value is 5%.

Value

hanr_histogram object histogram based method to detect anomalies in time series. Bins with smaller amount of observations are considered anomalies. Values below first bin or above last bin are also considered anomalies.>.

Examples

library(daltoolbox)

#loading the example database
data(examples_anomalies)

#Using simple example
dataset <- examples_anomalies$simple
head(dataset)
#>       serie event
#> 1 1.0000000 FALSE
#> 2 0.9689124 FALSE
#> 3 0.8775826 FALSE
#> 4 0.7316889 FALSE
#> 5 0.5403023 FALSE
#> 6 0.3153224 FALSE

# setting up time series regression model
model <- hanr_histogram()

# fitting the model
model <- fit(model, dataset$serie)

detection <- detect(model, dataset$serie)

# filtering detected events
print(detection[(detection$event),])
#>    idx event    type
#> 1    1  TRUE anomaly
#> 50  50  TRUE anomaly