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# Harbinger Package
# version 1.0.777

source("https://raw.githubusercontent.com/cefet-rj-dal/harbinger/master/jupyter.R")

#loading Harbinger
load_library("daltoolbox") 
load_library("harbinger")

Loading required package: daltoolbox

Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 


Attaching package: ‘daltoolbox’


The following object is masked from ‘package:base’:

    transform


Loading required package: harbinger

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#loading the example database
data(examples_anomalies)

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

A data.frame: 6 × 2
	serie	event
	<dbl>	<lgl>
1	1.0000000	FALSE
2	0.9689124	FALSE
3	0.8775826	FALSE
4	0.7316889	FALSE
5	0.5403023	FALSE
6	0.3153224	FALSE

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#ploting the time series
plot_ts(x = 1:length(dataset$serie), y = dataset$serie)

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model <- hanct_dtw(1)

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

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# making detections of anomalies using kmeans
  detection <- detect(model, dataset$serie)

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# filtering detected events
  print(detection |> dplyr::filter(event==TRUE))

  idx event    type
1  50  TRUE anomaly

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

          event      
detection TRUE  FALSE
TRUE      1     0    
FALSE     0     100

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

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