hcp_red: Anomaly and change point detector using RED

In harbinger: A Unified Time Series Event Detection Framework

Anomaly and change point detector using RED

Description

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)

# 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),])

harbinger documentation built on June 22, 2024, 7:38 p.m.