hanct_dtw: Anomaly detector using DTW

In harbinger: A Unified Time Series Event Detection Framework

Anomaly detector using DTW

Description

Anomaly detection using DTW The DTW is applied to the time series. When seq equals one, observations distant from the closest centroids are labeled as anomalies. When seq is grater than one, sequences distant from the closest centroids are labeled as discords. It wraps the tsclust presented in the dtwclust library.

Usage

hanct_dtw(seq = 1, centers = NA)

Arguments

seq	sequence size
centers	number of centroids

Value

hanct_dtw object

Examples

library(daltoolbox)

#loading the example database
data(examples_anomalies)

#Using simple example
dataset <- examples_anomalies$simple
head(dataset)

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

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

detection <- detect(model, dataset$serie)

# filtering detected events
print(detection[(detection$event),])

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