{ "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$serie)" ] }, { "cell_type": "code", "execution_count": 5, "id": "e0c8a397", "metadata": {}, "outputs": [], "source": [ "# establishing change finder arima method \n", " model <- hcp_cf_arima()" ] }, { "cell_type": "code", "execution_count": 6, "id": "9d0079cf", "metadata": {}, "outputs": [], "source": [ "# fitting the model\n", " model <- fit(model, dataset$serie)" ] }, { "cell_type": "code", "execution_count": 7, "id": "863506fd", "metadata": {}, "outputs": [], "source": [ "# making detections\n", " detection <- detect(model, dataset$serie)" ] }, { "cell_type": "code", "execution_count": 8, "id": "1241ed10", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " idx event type\n", "1 51 TRUE changepoint\n" ] } ], "source": [ "# filtering detected events\n", " print(detection |> dplyr::filter(event==TRUE))" ] }, { "cell_type": "code", "execution_count": 9, "id": "10456892", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " event \n", "detection TRUE FALSE\n", "TRUE 0 1 \n", "FALSE 1 99 \n" ] } ], "source": [ "# evaluating the detections\n", " evaluation <- evaluate(model, detection$event, dataset$event)\n", " print(evaluation$confMatrix)" ] }, { "cell_type": "code", "execution_count": 10, "id": "075f7d2f", "metadata": {}, "outputs": [ { "data": { "image/png": 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qVXrlyR3QLgoTDsAPyLoqKiiIgIe3v7iIgI2S2oEM7OzkFBQQUFBVyRBawdww7A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3b//pp5926tRJUZQrV65I59wBww4AAOBB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\nYII=", "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 }