{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Configuración de Datos\n", "\n", "En este cuaderno, mostramos cómo:\n", "- configurar datos de series temporales para este módulo\n", "- visualizar los datos\n", "\n", "Los datos de este ejemplo provienen de la competencia de pronóstico GEFCom2014. \n", "Consisten en 3 años de valores horarios de carga eléctrica y temperatura entre 2012 y 2014. \n", "\n", "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli y Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, julio-septiembre, 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": [ "Cargar los datos del archivo CSV en un dataframe de 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": "
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