{
"cells": [
{
"cell_type": "markdown",
"id": "73bea3fa-a2fa-48c4-abba-9e16712abfd6",
"metadata": {},
"source": [
"\n",
"[](http://rpi.analyticsdojo.com)\n",
"Time Series Analysis
\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "97bcb674-2859-4092-abbb-53b1040dc276",
"metadata": {},
"source": [
"# Time Series Analysis"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "5d7b6608",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"import yfinance as yf\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from statsmodels.tsa.seasonal import seasonal_decompose"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "edbbf7eb",
"metadata": {},
"outputs": [],
"source": [
"#!pip install yfinance"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "06006ab7",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Open | \n",
" High | \n",
" Low | \n",
" Close | \n",
" Adj Close | \n",
" Volume | \n",
"
\n",
" \n",
" | Date | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | 2016-01-04 | \n",
" 15.381333 | \n",
" 15.425333 | \n",
" 14.600000 | \n",
" 14.894000 | \n",
" 14.894000 | \n",
" 102406500 | \n",
"
\n",
" \n",
" | 2016-01-05 | \n",
" 15.090667 | \n",
" 15.126000 | \n",
" 14.666667 | \n",
" 14.895333 | \n",
" 14.895333 | \n",
" 47802000 | \n",
"
\n",
" \n",
" | 2016-01-06 | \n",
" 14.666667 | \n",
" 14.670000 | \n",
" 14.398667 | \n",
" 14.602667 | \n",
" 14.602667 | \n",
" 56686500 | \n",
"
\n",
" \n",
" | 2016-01-07 | \n",
" 14.279333 | \n",
" 14.562667 | \n",
" 14.244667 | \n",
" 14.376667 | \n",
" 14.376667 | \n",
" 53314500 | \n",
"
\n",
" \n",
" | 2016-01-08 | \n",
" 14.524000 | \n",
" 14.696000 | \n",
" 14.051333 | \n",
" 14.066667 | \n",
" 14.066667 | \n",
" 54421500 | \n",
"
\n",
" \n",
"
\n",
"