325 lines
130 KiB
Plaintext
325 lines
130 KiB
Plaintext
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"cells": [
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"cell_type": "markdown",
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"source": [
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"___\n",
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"\n",
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"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
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"___\n",
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"<center>*Copyright Pierian Data 2017*</center>\n",
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"<center>*For more information, visit us at www.pieriandata.com*</center>"
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]
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},
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"cell_type": "code",
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"execution_count": 45,
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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},
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"outputs": [],
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"source": [
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"airline = pd.read_csv('airline_passengers.csv',index_col=\"Month\")"
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]
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},
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"cell_type": "code",
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"\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Thousands of Passengers</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Month</th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>1949-01</th>\n",
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" <td>112.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1949-02</th>\n",
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" <td>118.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1949-03</th>\n",
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" <td>132.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1949-04</th>\n",
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" <td>129.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1949-05</th>\n",
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" <td>121.0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Thousands of Passengers\n",
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"Month \n",
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"1949-01 112.0\n",
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"1949-02 118.0\n",
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"1949-03 132.0\n",
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"1949-04 129.0\n",
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"1949-05 121.0"
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}
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],
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"source": [
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"airline.head()"
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]
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},
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{
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"cell_type": "code",
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"data": {
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"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x119e33208>"
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]
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},
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"execution_count": 41,
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"metadata": {},
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"output_type": "execute_result"
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"data": {
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"text/plain": [
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"<matplotlib.figure.Figure at 0x119d35f60>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"airline.plot()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## ETS\n",
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"\n",
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"We can use an additive model when it seems that the trend is more linear and the seasonality and trend components seem to be constant over time (e.g. every year we add 10,000 passengers). A multiplicative model is more appropriate when we are increasing (or decreasing) at a non-linear rate (e.g. each year we double the amount of passengers).\n",
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"\n",
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"Based off this chart, it looks like the trend in these earlier days is slightly increasing at a higher rate than just linear (although it is a bit hard to tell from this one plot)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 51,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Get data in correct format\n",
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"airline.dropna(inplace=True)\n",
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"airline.index = pd.to_datetime(airline.index)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"data": {
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"text/html": [
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"<div>\n",
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"<style>\n",
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" .dataframe thead tr:only-child th {\n",
|
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" text-align: right;\n",
|
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|
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" }\n",
|
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|
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"\n",
|
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|
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" .dataframe thead th {\n",
|
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" text-align: left;\n",
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" }\n",
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"\n",
|
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" <thead>\n",
|
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|
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Thousands of Passengers</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Month</th>\n",
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" <th></th>\n",
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" </tr>\n",
|
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|
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" </thead>\n",
|
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|
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" <tbody>\n",
|
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|
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" <tr>\n",
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" <th>1949-01-01</th>\n",
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" <td>112.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1949-02-01</th>\n",
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" <td>118.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1949-03-01</th>\n",
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" <td>132.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1949-04-01</th>\n",
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" <td>129.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1949-05-01</th>\n",
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" <td>121.0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
|
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"</div>"
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],
|
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"text/plain": [
|
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" Thousands of Passengers\n",
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|
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"Month \n",
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"1949-01-01 112.0\n",
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"1949-02-01 118.0\n",
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"1949-03-01 132.0\n",
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"1949-04-01 129.0\n",
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"1949-05-01 121.0"
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]
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},
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|
],
|
||
|
|
"source": [
|
||
|
|
"airline.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 58,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEZCAYAAAAt5touAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nb2hvPI8/iCFOh04n2N3
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11a63ea90>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 58,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEZCAYAAAAt5touAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nb2hvPI8/iCFOh04n2N3
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11a63ea90>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"from statsmodels.tsa.seasonal import seasonal_decompose\n",
|
||
|
|
"result = seasonal_decompose(airline['Thousands of Passengers'], model='multiplicative')\n",
|
||
|
|
"result.plot()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 59,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# You may accidentally see two of the same plots here, not to worry,\n",
|
||
|
|
"# just a small bug with statsmodels function."
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"anaconda-cloud": {},
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python [default]",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.5.3"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|