Title:	A Unified Time Series Event Detection Framework
Version:	1.2.727
Description:	By analyzing time series, it is possible to observe significant changes in the behavior of observations that frequently characterize events. Events present themselves as anomalies, change points, or motifs. In the literature, there are several methods for detecting events. However, searching for a suitable time series method is a complex task, especially considering that the nature of events is often unknown. This work presents Harbinger, a framework for integrating and analyzing event detection methods. Harbinger contains several state-of-the-art methods described in Salles et al. (2020) <doi:10.5753/sbbd.2020.13626>.
License:	MIT + file LICENSE
URL:	https://cefet-rj-dal.github.io/harbinger/, https://github.com/cefet-rj-dal/harbinger
BugReports:	https://github.com/cefet-rj-dal/harbinger/issues
Encoding:	UTF-8
Depends:	R (≥ 4.1.0)
RoxygenNote:	7.3.2
Imports:	tspredit, changepoint, daltoolbox, forecast, ggplot2, hht, RcppHungarian, dplyr, dtwclust, rugarch, stats, stringr, strucchange, tsmp, wavelets, zoo
NeedsCompilation:	no
Packaged:	2025-06-29 10:37:12 UTC; gpca
Author:	Eduardo Ogasawara [aut, ths, cre], Antonio Castro [aut], Antonio Mello [aut], Ellen Paixão [aut], Fernando Fraga [aut], Heraldo Borges [aut], Janio Lima [aut], Jessica Souza [aut], Lais Baroni [aut], Lucas Tavares [aut], Michel Reis [aut], Rebecca Salles [aut], Diego Carvalho [ctb], Eduardo Bezerra [ctb], Rafaelli Coutinho [ctb], Esther Pacitti [ctb], Fabio Porto [ctb], CEFET/RJ [cph]
Maintainer:	Eduardo Ogasawara <eogasawara@ieee.org>
Repository:	CRAN
Date/Publication:	2025-06-29 12:10:02 UTC

Detect events in time series

Description

Event detection using a fitted Harbinger model

Usage

detect(obj, ...)

Arguments

Value

a data frame with the index of observations and if they are identified or not as an event, and their type

Examples

#See examples of detectors for anomalies, change points, and motifs
#at https://cefet-rj-dal.github.io/harbinger

Time series for anomaly detection

Description

A list of time series for anomaly detection

• simple: a simple synthetic time series

• contextual: a contextual synthetic time series

• simple: a trend synthetic time series

• trend: a simple synthetic time series

• multiple: a multiple anomalies synthetic time series

• sequence: a sequence synthetic time series

• tt: a train-test synthetic time series

• tt_warped: a warped train-test synthetic time series

#'

Usage

data(examples_anomalies)

Format

A list of time series for anomaly detection.

Source

Harbinger package

References

Harbinger package

Examples

data(examples_anomalies)
serie <- examples_anomalies$simple

Time series for change point detection

Description

A list of time series for change point

• simple: a simple synthetic time series

• sinusoidal: a sinusoidal synthetic time series

• incremental: a incremental synthetic time series

• abrupt: a abrupt synthetic time series

• volatility: a volatility synthetic time series

#'

Usage

data(examples_changepoints)

Format

A list of time series for change point detection.

Source

Harbinger package

References

Harbinger package

Examples

data(examples_changepoints)
serie <- examples_changepoints$simple

Time series for event detection

Description

A list of time series for event detection

• nonstationarity: a synthetic nonstationarity time series

• global_temperature_yearly: yearly global temperature of the world

• global_temperature_monthly: monthly global temperature of the world

• multidimensional: multidimensional time series with a change point

• seattle_week: Seattle weakly temperature in 2019

• seattle_daily: Seattle daily temperature in 2019

#'

Usage

data(examples_harbinger)

Format

A list of time series.

Source

Harbinger package

References

Harbinger package

Examples

data(examples_harbinger)
serie <- examples_harbinger$seattle_daily

Time series for change point detection

Description

A list of time series for change point

• simple: a simple synthetic time series

• mitdb100: sample of mitdb 100 time series

• mitdb102: sample of mitdb 102 time series

#'

Usage

data(examples_motifs)

Format

A list of time series for motif discovery.

Source

Harbinger package

References

Harbinger package

Examples

data(examples_motifs)
serie <- examples_motifs$simple

Anomaly detector using autoencoder

Description

Anomaly detector using autoencoder

Usage

han_autoencoder(input_size, encode_size, encoderclass = autoenc_base_ed, ...)

Arguments

Value

han_autoencoder object histogram based method to detect anomalies in time series. Bins with smaller amount of observations are considered anomalies. Values below first bin or above last bin are also considered anomalies.>.

Examples

#See an example of using `autoenc_ed` at this
#https://github.com/cefet-rj-dal/harbinger/blob/master/anomalies/han_autoenc_ed

Anomaly detector based on machine learning classification

Description

Anomaly detection using daltoolbox classification. A training and test set should be used. The training set must contain labeled events. A set of preconfigured of classification methods are described in https://cefet-rj-dal.github.io/daltoolbox/. They include: cla_majority, cla_dtree, cla_knn, cla_mlp, cla_nb, cla_rf, cla_svm

Usage

hanc_ml(model, threshold = 0.5)

Arguments

Value

hanc_ml object

Examples

library(daltoolbox)

#loading the example database
data(examples_anomalies)

#Using example tt
dataset <- examples_anomalies$tt
dataset$event <- factor(dataset$event, labels=c("FALSE", "TRUE"))
slevels <- levels(dataset$event)

# separating into training and test
train <- dataset[1:80,]
test <- dataset[-(1:80),]

# normalizing the data
norm <- minmax()
norm <- fit(norm, train)
train_n <- daltoolbox::transform(norm, train)

# establishing decision tree method
model <- hanc_ml(cla_dtree("event", slevels))

# fitting the model
model <- fit(model, train_n)

# evaluating the detections during testing
test_n <- daltoolbox::transform(norm, test)

detection <- detect(model, test_n)
print(detection[(detection$event),])

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

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),])

Anomaly detector using kmeans

Description

Anomaly detection using kmeans The kmeans 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 kmeans presented in the stats library.

Usage

hanct_kmeans(seq = 1, centers = NA)

Arguments

Value

hanct_kmeans 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_kmeans()

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

detection <- detect(model, dataset$serie)

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

Anomaly detector using ARIMA.

Description

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

Usage

hanr_arima()

Value

hanr_arima 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 <- hanr_arima()

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

detection <- detect(model, dataset$serie)

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

Anomaly detector using EMD

Description

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

Value

hanr_emd object

Examples

library(daltoolbox)

#loading the example database
data(examples_anomalies)

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

# 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),])

Anomaly detector using FBIAD

Description

Anomaly detector using FBIAD

Usage

hanr_fbiad(sw_size = 30)

Arguments

Value

hanr_fbiad object Forward and Backward Inertial Anomaly Detector (FBIAD) detects anomalies in time series. Anomalies are observations that differ from both forward and backward time series inertia doi:10.1109/IJCNN55064.2022.9892088.

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 <- hanr_fbiad()

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

detection <- detect(model, dataset$serie)

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

Anomaly detector using FFT

Description

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

Usage

hanr_fft()

Value

hanr_fft object

Examples

library(daltoolbox)

#loading the example database
data(examples_anomalies)

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

# setting up time series fft detector
model <- hanr_fft()

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

detection <- detect(model, dataset$serie)

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

Anomaly Detector using FFT with AMOC Cutoff

Description

This function implements an anomaly detection method that uses the Fast Fourier daltoolbox::transform (FFT) combined with an automatic frequency cutoff strategy based on the AMOC (At Most One Change) algorithm. The model analyzes the power spectrum of the time series and detects the optimal cutoff frequency — the point where the frequency content significantly changes — using a changepoint detection method from the changepoint package.

All frequencies below the cutoff are removed from the spectrum, and the inverse FFT reconstructs a filtered version of the original signal that preserves only high-frequency components. The resulting residual signal is then analyzed to identify anomalous patterns based on its distance from the expected behavior.

This function extends the HARBINGER framework and returns an object of class hanr_fft_amoc.

Usage

hanr_fft_amoc()

Value

hanr_fft_amoc object

Examples

library(daltoolbox)

#loading the example database
data(examples_anomalies)

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

# setting up time series fft detector
model <- hanr_fft_amoc()

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

detection <- detect(model, dataset$serie)

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

Anomaly Detector using FFT with AMOC and CUSUM Cutoff

Description

This function implements an anomaly detection method based on the Fast Fourier daltoolbox::transform (FFT) and a changepoint-based cutoff strategy using the AMOC (At Most One Change) algorithm applied to the cumulative sum (CUSUM) of the power spectrum.

The method first computes the FFT of the input time series and extracts the power spectrum. It then applies a CUSUM transformation to the spectral data to emphasize gradual changes or shifts in spectral energy. Using the AMOC algorithm, it detects a single changepoint in the CUSUM-transformed spectrum, which serves as a cutoff index to remove the lower-frequency components.

The remaining high-frequency components are then reconstructed into a time-domain signal via inverse FFT, effectively isolating rapid or local deviations. Anomalies are detected by evaluating the distance between this filtered signal and the original series, highlighting points that deviate significantly from the expected pattern.

This method is suitable for series where spectral shifts are subtle and a single significant change in behavior is expected.

This function extends the HARBINGER framework and returns an object of class hanr_fft_amoc_cusum.

Usage

hanr_fft_amoc_cusum()

Value

hanr_fft_amoc_cusum object

Examples

library(daltoolbox)

#loading the example database
data(examples_anomalies)

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

# setting up time series fft detector
model <- hanr_fft_amoc_cusum()

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

detection <- detect(model, dataset$serie)

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

Anomaly Detector using FFT with Binary Segmentation Cutoff

Description

This function implements an anomaly detection method that combines the Fast Fourier daltoolbox::transform (FFT) with a spectral cutoff strategy based on the Binary Segmentation (BinSeg) algorithm for changepoint detection.

The method analyzes the power spectrum of the input time series and applies the BinSeg algorithm to identify a changepoint in the spectral density, corresponding to a shift in the frequency content. Frequencies below this changepoint are considered part of the underlying trend or noise and are removed from the signal.

The modified spectrum is then transformed back into the time domain via inverse FFT, resulting in a high-pass filtered version of the series. Anomalies are identified by measuring the distance between the original and the filtered signal, highlighting unusual deviations from the dominant signal behavior.

This function is part of the HARBINGER framework and returns an object of class hanr_fft_binseg.

Usage

hanr_fft_binseg()

Value

hanr_fft_binseg object

Examples

library(daltoolbox)

#loading the example database
data(examples_anomalies)

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

# setting up time series fft detector
model <- hanr_fft_binseg()

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

detection <- detect(model, dataset$serie)

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

Anomaly Detector using FFT with BinSeg and CUSUM Cutoff

Description

This function implements an anomaly detection method that combines the Fast Fourier daltoolbox::transform (FFT) with a changepoint-based cutoff strategy using the Binary Segmentation (BinSeg) method applied to the cumulative sum (CUSUM) of the frequency spectrum.

The method first computes the FFT of the input time series and obtains its power spectrum. Then, it applies a CUSUM transformation to the spectral density to enhance detection of gradual transitions or accumulated changes in energy across frequencies. The Binary Segmentation method is applied to the CUSUM-transformed spectrum to identify a changepoint that defines a cutoff frequency.

Frequencies below this cutoff are removed from the spectrum, and the signal is reconstructed using the inverse FFT. This produces a filtered signal that retains only the high-frequency components, emphasizing potential anomalies.

Anomalies are then detected by measuring the deviation of the filtered signal from the original one, and applying an outlier detection mechanism based on this residual.

This function extends the HARBINGER framework and returns an object of class hanr_fft_binseg_cusum.

Usage

hanr_fft_binseg_cusum()

Value

hanr_fft_binseg_cusum object

Examples

library(daltoolbox)

#loading the example database
data(examples_anomalies)

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

# setting up time series fft detector
model <- hanr_fft_binseg_cusum()

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

detection <- detect(model, dataset$serie)

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

Anomaly Detector using Adaptive FFT and Moving Average

Description

This function implements an anomaly detection model based on the Fast Fourier daltoolbox::transform (FFT), combined with an adaptive moving average filter. The method estimates the dominant frequency in the input time series using spectral analysis and then applies a moving average filter with a window size derived from that frequency. This highlights high-frequency deviations, which are likely to be anomalies.

The residuals (original signal minus smoothed version) are then processed to compute the distance from the expected behavior, and points significantly distant are flagged as anomalies. The detection also includes a grouping strategy to reduce false positives by selecting the most representative point in a cluster of consecutive anomalies.

This function extends the HARBINGER framework and returns an object of class hanr_fft_sma.

Usage

hanr_fft_sma()

Value

hanr_fft_sma object

Examples

library(daltoolbox)

#loading the example database
data(examples_anomalies)

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

# setting up time series fft detector
model <- hanr_fft_sma()

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

detection <- detect(model, dataset$serie)

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

Anomaly detector using GARCH

Description

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

Usage

hanr_garch()

Value

hanr_garch 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 <- hanr_garch()

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

detection <- detect(model, dataset$serie)

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

Anomaly detector using histogram

Description

Anomaly detector using histogram

Usage

hanr_histogram(density_threshold = 0.05)

Arguments

Value

hanr_histogram object histogram based method to detect anomalies in time series. Bins with smaller amount of observations are considered anomalies. Values below first bin or above last bin are also considered anomalies.>.

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 <- hanr_histogram()

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

detection <- detect(model, dataset$serie)

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

Anomaly detector based on machine learning regression.

Description

Anomaly detection using daltoolbox regression The regression model adjusts to the time series. Observations distant from the model are labeled as anomalies. A set of preconfigured regression methods are described in https://cefet-rj-dal.github.io/daltoolbox/. They include: ts_elm, ts_conv1d, ts_lstm, ts_mlp, ts_rf, ts_svm

Usage

hanr_ml(model, sw_size = 15)

Arguments

Value

hanr_ml object

Examples

library(daltoolbox)
library(tspredit)

#loading the example database
data(examples_anomalies)

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

# setting up time series regression model
model <- hanr_ml(tspredit::ts_elm(tspredit::ts_norm_gminmax(),
                  input_size=4, nhid=3, actfun="purelin"))

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

detection <- detect(model, dataset$serie)

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

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

hanr_red(sw_size = 30, noise = 0.001, trials = 5)

Arguments

Value

hanr_red object

Examples

library(daltoolbox)
library(zoo)

#loading the example database
data(examples_anomalies)

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

# setting up time series emd detector
model <- hanr_red()

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

detection <- detect(model, dataset$serie)

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

Anomaly detector using REMD

Description

Anomaly detection using REMD 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 forecast library.

Usage

hanr_remd(noise = 0.1, trials = 5)

Arguments

Value

hanr_remd object

Examples

library(daltoolbox)

#loading the example database
data(examples_anomalies)

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

# setting up time series emd detector
model <- hanr_remd()

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

detection <- detect(model, dataset$serie)

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

Anomaly detector using Wavelet

Description

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

Usage

hanr_wavelet(filter = "haar")

Arguments

Value

hanr_wavelet object

Examples

library(daltoolbox)

#loading the example database
data(examples_anomalies)

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

# setting up time series fft detector
model <- hanr_wavelet()

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

detection <- detect(model, dataset$serie)

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

Harbinger Ensemble

Description

Ensemble detector

Usage

har_ensemble(...)

Arguments

Value

Harbinger object

Examples

library(daltoolbox)

#loading the example database
data(examples_anomalies)

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

# setting up time series emd detector
model <- har_ensemble(hanr_arima(), hanr_arima(), hanr_arima())
#model <- har_ensemble(hanr_fbiad(), hanr_arima(), hanr_emd())

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

detection <- detect(model, dataset$serie)

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

Evaluation of event detection

Description

Evaluation of event detection (traditional hard evaluation)

Usage

har_eval()

Value

har_eval object

Examples

library(daltoolbox)

#loading the example database
data(examples_anomalies)

dataset <- examples_anomalies$simple
head(dataset)

# setting up time change point using GARCH
model <- hcp_garch()

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

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

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

# evaluating the detections
evaluation <- evaluate(har_eval(), detection$event, dataset$event)
print(evaluation$confMatrix)

# ploting the results
grf <- har_plot(model, dataset$serie, detection, dataset$event)
plot(grf)

Evaluation of event detection

Description

Evaluation of event detection using SoftED doi:10.48550/arXiv.2304.00439

Usage

har_eval_soft(sw_size = 15)

Arguments

Value

har_eval_soft object

Examples

library(daltoolbox)

#loading the example database
data(examples_anomalies)

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

# setting up time change point using GARCH
model <- hcp_garch()

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

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

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

# evaluating the detections
evaluation <- evaluate(har_eval_soft(), detection$event, dataset$event)
print(evaluation$confMatrix)

# ploting the results
grf <- har_plot(model, dataset$serie, detection, dataset$event)
plot(grf)

Plot event detection on a time series

Description

It accepts as harbinger, a time series, a data.frame of events, a parameter to mark the detected change points, a threshold for the y-axis and an index for the time series

Usage

har_plot(
  obj,
  serie,
  detection = NULL,
  event = NULL,
  mark.cp = TRUE,
  ylim = NULL,
  idx = NULL,
  pointsize = 0.5,
  colors = c("green", "blue", "red", "purple"),
  yline = NULL
)

Arguments

Value

A time series plot with marked events

Examples

library(daltoolbox)

#loading the example database
data(examples_anomalies)

#Using the simple time series
dataset <- examples_anomalies$simple
head(dataset)

# setting up time change point using GARCH
model <- hanr_arima()

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

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

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

# evaluating the detections
evaluation <- evaluate(har_eval_soft(), detection$event, dataset$event)
print(evaluation$confMatrix)

# ploting the results
grf <- har_plot(model, dataset$serie, detection, dataset$event)
plot(grf)

Harbinger

Description

Ancestor class for time series event detection

Usage

harbinger()

Value

Harbinger object

Examples

#See examples of detectors for anomalies, change points, and motifs
#at https://cefet-rj-dal.github.io/harbinger

Harbinger Utils

Description

Utility class that contains major distance measures, threshold limits, and outliers grouping functions

Usage

harutils()

Value

Harbinger Utils

Examples

# See ?hanc_ml for an example of anomaly detection using machine learning classification

At most one change (AMOC) method

Description

Change-point detection method that focus on identify one change point in mean/variance doi:10.1093/biomet/57.1.1. It wraps the amoc implementation available in the changepoint library.

Usage

hcp_amoc()

Value

hcp_amoc 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_amoc()

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

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

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

Binary segmentation (BinSeg) method

Description

Change-point detection method that focus on identify change points in mean/variance doi:10.2307/2529204. It wraps the BinSeg implementation available in the changepoint library.

Usage

hcp_binseg(Q = 2)

Arguments

Value

hcp_binseg 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_binseg()

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

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

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

Change Finder using ARIMA

Description

Change-point detection is related to event/trend change detection. Change Finder ARIMA detects change points based on deviations relative to ARIMA model doi:10.1109/TKDE.2006.1599387. It wraps the ARIMA model presented in the forecast library.

Usage

hcp_cf_arima(sw_size = NULL)

Arguments

Value

hcp_cf_arima 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_cf_arima()

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

detection <- detect(model, dataset$serie)

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

Change Finder using ETS

Description

Change-point detection is related to event/trend change detection. Change Finder ETS detects change points based on deviations relative to trend component (T), a seasonal component (S), and an error term (E) model doi:10.1109/TKDE.2006.1599387. It wraps the ETS model presented in the forecast library.

Usage

hcp_cf_ets(sw_size = 7)

Arguments

Value

hcp_cf_ets 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_cf_ets()

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

detection <- detect(model, dataset$serie)

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

Change Finder using LR

Description

Change-point detection is related to event/trend change detection. Change Finder LR detects change points based on deviations relative to linear regression model doi:10.1109/TKDE.2006.1599387.

Usage

hcp_cf_lr(sw_size = 30)

Arguments

Value

hcp_cf_lr 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_cf_lr()

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

detection <- detect(model, dataset$serie)

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

Chow test method

Description

Change-point detection method that focus on identifying structural changes doi:10.18637/jss.v007.i02. It wraps the Fstats and breakpoints implementation available in the strucchange library.

Usage

hcp_chow()

Value

hcp_chow 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_chow()

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

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

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

Change Finder using GARCH

Description

Change-point detection is related to event/trend change detection. Change Finder GARCH detects change points based on deviations relative to linear regression model doi:10.1109/TKDE.2006.1599387. It wraps the GARCH model presented in the rugarch library.

Usage

hcp_garch(sw_size = 5)

Arguments

Value

hcp_garch object

Examples

library(daltoolbox)

#loading the example database
data(examples_changepoints)

#Using volatility example
dataset <- examples_changepoints$volatility
head(dataset)

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

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

detection <- detect(model, dataset$serie)

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

Generalized Fluctuation Test (GFT)

Description

GFT detection method focuses on identifying structural changes doi:10.18637/jss.v007.i02. It wraps the breakpoints implementation available in the strucchange library.

Usage

hcp_gft()

Value

hcp_chow 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_gft()

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

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

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

Pruned exact linear time (PELT) method

Description

Change-point detection method that focus on identifying multiple exact change points in mean/variance doi:10.1080/01621459.2012.737745. It wraps the BinSeg implementation available in the changepoint library.

Usage

hcp_pelt()

Value

hcp_pelt 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_pelt()

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

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

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

Seminal change point

Description

Change-point detection is related to event/trend change detection. Seminal change point detects change points based on deviations of linear regression models adjusted with and without a central observation in each sliding window <10.1145/312129.312190>.

Usage

hcp_scp(sw_size = 30)

Arguments

Value

hcp_scp 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_scp()

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

detection <- detect(model, dataset$serie)

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

Discord discovery using Matrix Profile

Description

Discord discovery using Matrix Profile doi:10.32614/RJ-2020-021

Usage

hdis_mp(mode = "stamp", w, qtd)

Arguments

Value

hdis_mp object

Examples

library(daltoolbox)

#loading the example database
data(examples_motifs)

#Using sequence example
dataset <- examples_motifs$simple
head(dataset)

# setting up discord discovery method
model <- hdis_mp("stamp", 4, 3)

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

detection <- detect(model, dataset$serie)

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

Discord discovery using SAX

Description

Discord discovery using SAX doi:10.1007/s10618-007-0064-z

Usage

hdis_sax(a, w, qtd = 2)

Arguments

Value

hdis_sax object

Examples

library(daltoolbox)

#loading the example database
data(examples_motifs)

#Using sequence example
dataset <- examples_motifs$simple
head(dataset)

# setting up discord discovery method
model <- hdis_sax(26, 3, 3)

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

detection <- detect(model, dataset$serie)

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

Motif discovery using Matrix Profile

Description

Motif discovery using Matrix Profile doi:10.32614/RJ-2020-021

Usage

hmo_mp(mode = "stamp", w, qtd)

Arguments

Value

hmo_mp object

Examples

library(daltoolbox)

#loading the example database
data(examples_motifs)

#Using sequence example
dataset <- examples_motifs$simple
head(dataset)

# setting up motif discovery method
model <- hmo_mp("stamp", 4, 3)

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

detection <- detect(model, dataset$serie)

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

Motif discovery using SAX

Description

Motif discovery using SAX doi:10.1007/s10618-007-0064-z

Usage

hmo_sax(a, w, qtd = 2)

Arguments

Value

hmo_sax object

Examples

library(daltoolbox)

#loading the example database
data(examples_motifs)

#Using sequence example
dataset <- examples_motifs$simple
head(dataset)

# setting up motif discovery method
model <- hmo_sax(26, 3, 3)

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

detection <- detect(model, dataset$serie)

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

Motif discovery using xsax

Description

Motif discovery using xsax doi:10.1007/s10618-007-0064-z

Usage

hmo_xsax(a, w, qtd)

Arguments

Value

hmo_xsax object

Examples

library(daltoolbox)

#loading the example database
data(examples_motifs)

#Using sequence example
dataset <- examples_motifs$simple
head(dataset)

# setting up motif discovery method
model <- hmo_xsax(37, 3, 3)

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

detection <- detect(model, dataset$serie)

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

Multivariate anomaly detector using PCA

Description

Multivariate anomaly detector using PCA doi:10.1016/0098-3004(93)90090-R

Usage

hmu_pca()

Value

hmu_pca object

Examples

library(daltoolbox)

#loading the example database
data(examples_harbinger)

#Using the time series 9
dataset <- examples_harbinger$multidimensional
head(dataset)

# establishing hmu_pca method
model <- hmu_pca()

# fitting the model using the two columns of the dataset
model <- fit(model, dataset[,1:2])

# making detections
detection <- detect(model, dataset[,1:2])

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

# evaluating the detections
evaluation <- evaluate(model, detection$event, dataset$event)
print(evaluation$confMatrix)

Moving average smoothing

Description

The mas() function returns a simple moving average smoother of the provided time series.

Usage

mas(x, order)

Arguments

Details

The moving average smoother transformation is given by

(1/k) * ( x[t] + x[t+1] + ... + x[t+k-1] )

where k=order, t assume values in the range 1:(n-k+1), and n=length(x). See also the ma of the forecast package.

Value

Numerical time series of length length(x)-order+1 containing the simple moving average smoothed values.

References

R.H. Shumway and D.S. Stoffer, 2010, Time Series Analysis and Its Applications: With R Examples. 3rd ed. 2011 edition ed. New York, Springer.

Examples

#loading the example database
data(examples_changepoints)

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

# setting up change point method
ma <- mas(dataset$serie, 5)

SAX

Description

SAX

Usage

trans_sax(alpha)

Arguments

Value

obj

Examples

library(daltoolbox)
vector <- 1:52
model <- trans_sax(alpha = 26)
model <- fit(model, vector)
xvector <- transform(model, vector)
print(xvector)

XSAX

Description

XSAX

Usage

trans_xsax(alpha)

Arguments

Value

obj

Examples

library(daltoolbox)
vector <- 1:52
model <- trans_xsax(alpha = 52)
model <- fit(model, vector)
xvector <- transform(model, vector)
print(xvector)