{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# डेटा सेटअप\n",
"\n",
"या नोटबुकमध्ये, आम्ही कसे करायचे ते दाखवतो:\n",
"- या मॉड्यूलसाठी टाइम सिरीज डेटाचे सेटअप करणे\n",
"- डेटाचे व्हिज्युअलायझेशन करणे\n",
"\n",
"या उदाहरणातील डेटा GEFCom2014 अंदाज वर्तविण्याच्या स्पर्धेतून घेतलेला आहे. \n",
"तो 2012 ते 2014 या कालावधीत 3 वर्षांच्या तासागणिक वीज लोड आणि तापमानाच्या मूल्यांचा समावेश करतो. \n",
"\n",
"Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli आणि 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 मधून डेटा Pandas डेटा फ्रेममध्ये लोड करा\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