{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "1d0d0503", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loading required package: daltoolbox\n", "\n", "Registered S3 method overwritten by 'quantmod':\n", " method from\n", " as.zoo.data.frame zoo \n", "\n", "\n", "Attaching package: ‘daltoolbox’\n", "\n", "\n", "The following object is masked from ‘package:base’:\n", "\n", " transform\n", "\n", "\n", "Loading required package: harbinger\n", "\n" ] } ], "source": [ "# Harbinger Package\n", "# version 1.0.777\n", "\n", "source(\"https://raw.githubusercontent.com/cefet-rj-dal/harbinger/master/jupyter.R\")\n", "\n", "#loading Harbinger\n", "load_library(\"daltoolbox\") \n", "load_library(\"harbinger\") " ] }, { "cell_type": "code", "execution_count": 2, "id": "c0d41422", "metadata": {}, "outputs": [], "source": [ "#loading the example database\n", "data(examples_changepoints)" ] }, { "cell_type": "code", "execution_count": 3, "id": "9a12bab2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 2
serieevent
<dbl><lgl>
10.00FALSE
20.25FALSE
30.50FALSE
40.75FALSE
51.00FALSE
61.25FALSE
\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & serie & event\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & 0.00 & FALSE\\\\\n", "\t2 & 0.25 & FALSE\\\\\n", "\t3 & 0.50 & FALSE\\\\\n", "\t4 & 0.75 & FALSE\\\\\n", "\t5 & 1.00 & FALSE\\\\\n", "\t6 & 1.25 & FALSE\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| | serie <dbl> | event <lgl> |\n", "|---|---|---|\n", "| 1 | 0.00 | FALSE |\n", "| 2 | 0.25 | FALSE |\n", "| 3 | 0.50 | FALSE |\n", "| 4 | 0.75 | FALSE |\n", "| 5 | 1.00 | FALSE |\n", "| 6 | 1.25 | FALSE |\n", "\n" ], "text/plain": [ " serie event\n", "1 0.00 FALSE\n", "2 0.25 FALSE\n", "3 0.50 FALSE\n", "4 0.75 FALSE\n", "5 1.00 FALSE\n", "6 1.25 FALSE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Using the simple time series \n", "dataset <- examples_changepoints$simple\n", "head(dataset)" ] }, { "cell_type": "code", "execution_count": 4, "id": "b744a3ad", "metadata": {}, "outputs": [ { "data": { "image/png": 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v8oqvndBkUistjcj4uQvW/2sTn/ufup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\nKHWGU+hcpKyMm9l80gRApj978YBKX0T1LdKa6xMYq00YACjtSo5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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "#ploting the time series\n", "plot_ts(x = 1:length(dataset$serie), y = dataset$serie)" ] }, { "cell_type": "code", "execution_count": 5, "id": "e0c8a397", "metadata": {}, "outputs": [], "source": [ "# establishing change point method \n", " model <- hcp_red()" ] }, { "cell_type": "code", "execution_count": 6, "id": "9d0079cf", "metadata": {}, "outputs": [], "source": [ "# fitting the model\n", " model <- fit(model, dataset$serie)" ] }, { "cell_type": "code", "execution_count": 7, "id": "863506fd", "metadata": {}, "outputs": [], "source": [ "# making detections\n", " detection <- detect(model, dataset$serie)" ] }, { "cell_type": "code", "execution_count": 8, "id": "1241ed10", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " idx event type\n", "1 49 TRUE changepoint\n" ] } ], "source": [ "# filtering detected events\n", " print(detection |> dplyr::filter(event==TRUE))" ] }, { "cell_type": "code", "execution_count": 9, "id": "10456892", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " event \n", "detection TRUE FALSE\n", "TRUE 0 1 \n", "FALSE 1 99 \n" ] } ], "source": [ "# evaluating the detections\n", " evaluation <- evaluate(model, detection$event, dataset$event)\n", " print(evaluation$confMatrix)" ] }, { "cell_type": "code", "execution_count": 10, "id": "075f7d2f", "metadata": {}, "outputs": [ { "data": { "image/png": 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OwWmJmXl9eMGTNu3ryZnJwsuwXAvWLYAfh369atO3LkiOn68bJbYH6TJk1q0KDB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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "# ploting the results\n", " grf <- har_plot(model, dataset$serie, detection, dataset$event)\n", " plot(grf)" ] }, { "cell_type": "code", "execution_count": null, "id": "07c53f34", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "4.4.0" } }, "nbformat": 4, "nbformat_minor": 5 }