{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Dalam notebook ini, kami menunjukkan cara untuk:\n", "- menyiapkan data deret waktu untuk modul ini\n", "- memvisualisasikan data\n", "\n", "Data dalam contoh ini diambil dari kompetisi peramalan GEFCom2014. Data tersebut terdiri dari 3 tahun nilai beban listrik dan suhu per jam antara tahun 2012 dan 2014.\n", "\n", "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli, dan Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, hlm. 896-913, Juli-September, 2016.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import os\n", "import matplotlib.pyplot as plt\n", "from common.utils import load_data\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Muat data dari csv ke dalam dataframe Pandas\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " load\n", "2012-01-01 00:00:00 2698.0\n", "2012-01-01 01:00:00 2558.0\n", "2012-01-01 02:00:00 2444.0\n", "2012-01-01 03:00:00 2402.0\n", "2012-01-01 04:00:00 2403.0" ], "text/html": "
\n | load | \n
---|---|
2012-01-01 00:00:00 | \n2698.0 | \n
2012-01-01 01:00:00 | \n2558.0 | \n
2012-01-01 02:00:00 | \n2444.0 | \n
2012-01-01 03:00:00 | \n2402.0 | \n
2012-01-01 04:00:00 | \n2403.0 | \n