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Anomaly detection using EMD The EMD model adjusts to the time series. Observations distant from the model are labeled as anomalies. It wraps the EMD model presented in the hht library.

Usage

hanr_emd(noise = 0.1, trials = 5)

Arguments

noise

nosie

trials

trials

Value

hanr_emd object

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 emd detector
model <- hanr_emd()

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

detection <- detect(model, dataset$serie)

# filtering detected events
print(detection[(detection$event),])
#>     idx event    type
#> 19   19  TRUE anomaly
#> 21   21  TRUE anomaly
#> 50   50  TRUE anomaly
#> 53   53  TRUE anomaly
#> 80   80  TRUE anomaly
#> 91   91  TRUE anomaly
#> 100 100  TRUE anomaly