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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_changepoints)

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

A data.frame: 6 × 2
	serie	event
	<dbl>	<lgl>
1	0.00	FALSE
2	0.25	FALSE
3	0.50	FALSE
4	0.75	FALSE
5	1.00	FALSE
6	1.25	FALSE

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

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# establishing change point method 
  model <- hcp_pelt()

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

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

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

  idx event        type
1   9  TRUE changepoint
2  19  TRUE changepoint
3  29  TRUE changepoint
4  39  TRUE changepoint
5  60  TRUE changepoint
6  71  TRUE changepoint
7  81  TRUE changepoint
8  91  TRUE changepoint

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

          event      
detection TRUE  FALSE
TRUE      0     8    
FALSE     1     92

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

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