{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Nastavení dat\n", "\n", "V tomto notebooku si ukážeme, jak:\n", "- připravit časové řady dat pro tento modul\n", "- vizualizovat data\n", "\n", "Data v tomto příkladu pocházejí ze soutěže GEFCom2014 v předpovídání. Skládají se z 3 let hodinových hodnot spotřeby elektřiny a teplot mezi lety 2012 a 2014.\n", "\n", "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli a Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, sv.32, č.3, str. 896-913, červenec-září, 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": [ "Načtěte data z CSV do Pandas dataframe.\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 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