{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Configuration des données\n", "\n", "Dans ce notebook, nous montrons comment :\n", "- configurer des données de séries temporelles pour ce module\n", "- visualiser les données\n", "\n", "Les données de cet exemple proviennent de la compétition de prévision GEFCom2014. Elles consistent en 3 années de charges électriques horaires et de valeurs de température entre 2012 et 2014.\n", "\n", "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli et Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, juillet-septembre, 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": [ "Charger les données du fichier CSV dans un 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": "
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