Anomaly and change point detector using RED

Anomaly and change point detection using RED The RED 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

hcp_red(
  sw_size = 30,
  noise = 0.001,
  trials = 5,
  red_cp = TRUE,
  volatility_cp = TRUE,
  trend_cp = TRUE
)

Arguments

sw_size

sliding window size (default 30)

noise

noise

trials

trials

red_cp

red change point

volatility_cp

volatility change point

trend_cp

trend change point

Value

hcp_red object

Examples

library(daltoolbox)

#loading the example database
data(examples_changepoints)

#Using simple example
dataset <- examples_changepoints$simple
head(dataset)
#>   serie event
#> 1  0.00 FALSE
#> 2  0.25 FALSE
#> 3  0.50 FALSE
#> 4  0.75 FALSE
#> 5  1.00 FALSE
#> 6  1.25 FALSE

# setting up change point method
model <- hcp_red()

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

# execute the detection method
detection <- detect(model, dataset$serie)

# filtering detected events
print(detection[(detection$event),])
#>    idx event        type
#> 51  51  TRUE changepoint