{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# डेटा सेटअप\n",
"\n",
"यस नोटबुकमा, हामीले कसरी:\n",
"- यो मोड्युलका लागि समय श्रृंखला डेटा सेटअप गर्ने\n",
"- डेटा दृश्यात्मक बनाउने\n",
"\n",
"यस उदाहरणमा प्रयोग गरिएको डेटा GEFCom2014 पूर्वानुमान प्रतियोगिताबाट लिइएको हो। \n",
"यसमा २०१२ देखि २०१४ सम्मको ३ वर्षको घण्टाको आधारमा बिजुली लोड र तापक्रमका मानहरू समावेश छन्। \n",
"\n",
"Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli र Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-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": [
"CSV बाट डेटा Pandas को डेटा फ्रेममा लोड गर्नुहोस्\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