308 lines
128 KiB
Plaintext
308 lines
128 KiB
Plaintext
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{
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"cells": [
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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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"# Décomposition ETS"
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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": 1,
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"metadata": {},
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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": 2,
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"metadata": {},
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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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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\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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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\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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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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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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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x114119210>"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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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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"Nous pouvons utiliser un modèle additif lorsqu'il semble que la tendance est plus linéaire et que les composantes saisonnières et tendancielles semblent être constantes dans le temps (par exemple: chaque année, nous ajoutons 10 000 passagers). Un modèle multiplicatif est plus approprié lorsque nous augmentons (ou diminuons) à un rythme non linéaire (par exemple: chaque année, nous doublons le nombre de passagers).\n",
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"\n",
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"D'après ce graphique, il semble que la tendance de ces premiers jours soit légèrement à la hausse à un taux plus élevé que le taux linéaire (bien qu'il soit un peu difficile de le dire à partir de cette seule courbe)."
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Obtenir les données dans un format correct\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": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\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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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
|
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|
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"</style>\n",
|
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|
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"<table border=\"1\" class=\"dataframe\">\n",
|
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|
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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>1949-01-01</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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"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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},
|
||
|
|
"execution_count": 6,
|
||
|
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"metadata": {},
|
||
|
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"output_type": "execute_result"
|
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|
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}
|
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|
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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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},
|
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|
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{
|
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"cell_type": "code",
|
||
|
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"execution_count": 7,
|
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"metadata": {},
|
||
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"outputs": [
|
||
|
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{
|
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"data": {
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|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"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": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# Vous risquez de voir accidentellement deux fois le même graphique ici, ne vous inquiétez pas,\n",
|
||
|
|
"# Juste un petit bug avec la fonction statsmodels."
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"anaconda-cloud": {},
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"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": "3.7.5"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|