Anomaly and change point detector using RTAD

Anomaly and change point detection using RTAD The RTAD 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_rtad(sw_size = 30, noise = 0.001, trials = 5, sigma = sd)

Arguments

sw_size

sliding window size (default 30)

noise

noise

trials

trials

sigma

function to compute the dispersion

Value

hanr_rtad object

References

• Ogasawara, E., Salles, R., Porto, F., Pacitti, E. Event Detection in Time Series. 1st ed. Cham: Springer Nature Switzerland, 2025. doi:10.1007/978-3-031-75941-3

Examples

library(daltoolbox)
library(zoo)
#> 
#> Attaching package: ‘zoo’
#> The following objects are masked from ‘package:base’:
#> 
#>     as.Date, as.Date.numeric

# Load anomaly example data
data(examples_anomalies)

# Use a 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

# Configure RTAD detector
model <- hanr_rtad()

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

# Run detection
detection <- detect(model, dataset$serie)

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