{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Datainställning\n", "\n", "I den här notebooken visar vi hur man:\n", "- ställer in tidsseriedata för den här modulen\n", "- visualiserar data\n", "\n", "Datan i det här exemplet är hämtad från GEFCom2014 prognostävling. Den består av 3 års timvisa elförbruknings- och temperaturvärden mellan 2012 och 2014.\n", "\n", "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli och Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, s. 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": [ "Ladda data från csv till en Pandas-dataram\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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2012-01-01 01:00:00 | \n2558.0 | \n
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