{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"在本筆記本中，我們將展示如何：\n",
"- 設置此模組的時間序列數據\n",
"- 可視化數據\n",
"\n",
"此範例中的數據取自GEFCom2014預測競賽。它包含2012年至2014年間3年的每小時電力負載和溫度值。\n",
"\n",
"陶宏、Pierre Pinson、Shu Fan、Hamidreza Zareipour、Alberto Troccoli和Rob J. Hyndman，\"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\"，《國際預測期刊》，第32卷，第3期，頁896-913，2016年7月至9月。\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": [
"將數據從 csv 加載到 Pandas 數據框中\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\n
\n \n \n | \n load | \n
\n \n \n \n | 2012-01-01 00:00:00 | \n 2698.0 | \n
\n \n | 2012-01-01 01:00:00 | \n 2558.0 | \n
\n \n | 2012-01-01 02:00:00 | \n 2444.0 | \n
\n \n | 2012-01-01 03:00:00 | \n 2402.0 | \n
\n \n | 2012-01-01 04:00:00 | \n 2403.0 | \n
\n \n
\n