AR ou MA

Dans cet exercice, vous allez utiliser l’ACF et la PACF pour décider si des données conviennent mieux à un modèle MA ou à un modèle AR. Rappelez-vous que choisir le bon ordre du modèle est crucial pour la qualité des prévisions.

Pour différents types de modèles, on attend le comportement suivant dans l’ACF et la PACF :

	AR(p)	MA(q)	ARMA(p,q)
ACF	Décroît progressivement	S’annule après le retard q	Décroît progressivement
PACF	S’annule après le retard p	Décroît progressivement	Décroît progressivement

Une série temporelle aux propriétés inconnues, df, est disponible dans votre environnement.

Cet exercice fait partie du cours

<cours>Modèles ARIMA en Python</cours>

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

# Import
from statsmodels.graphics.tsaplots import ____, ____

# Create figure
fig, (ax1, ax2) = plt.subplots(2,1, figsize=(12,8))
 
# Plot the ACF of df
____(____, lags=____, zero=False, ax=ax1)

# Plot the PACF of df
____(____, lags=____, zero=____, ax=ax2)

plt.show()

Modifier et exécuter le code

Cet exercice fait partie du cours

<cours>Modèles ARIMA en Python</cours>

AvancéNiveau de compétence

4.9+

Commencer le cours gratuitement

Dive straight in and learn about the most important properties of time series. You'll learn about stationarity and how this is important for ARMA models. You'll learn how to test for stationarity by eye and with a standard statistical test. Finally, you'll learn the basic structure of ARMA models and use this to generate some ARMA data and fit an ARMA model.

Exercise 1: Intro to time series and stationarity Exercise 2: Exploration Exercise 3: Train-test splits Exercise 4: Is it stationary Exercise 5: Making time series stationary Exercise 6: Augmented Dicky-Fuller Exercise 7: Taking the difference Exercise 8: Other tranforms Exercise 9: Intro to AR, MA and ARMA models Exercise 10: Model order Exercise 11: Generating ARMA data Exercise 12: Fitting Prelude

What lies ahead in this chapter is you predicting what lies ahead in your data. You'll learn how to use the elegant statsmodels package to fit ARMA, ARIMA and ARMAX models. Then you'll use your models to predict the uncertain future of stock prices!

Exercise 1: Fitting time series models Exercise 2: Fitting AR and MA models Exercise 3: Fitting an ARMA model Exercise 4: Fitting an ARMAX model Exercise 5: Forecasting Exercise 6: Generating one-step-ahead predictions Exercise 7: Plotting one-step-ahead predictions Exercise 8: Generating dynamic forecasts Exercise 9: Plotting dynamic forecasts Exercise 10: Intro to ARIMA models Exercise 11: Differencing and fitting ARMA Exercise 12: Unrolling ARMA forecast Exercise 13: Fitting an ARIMA model Exercise 14: Choosing ARIMA model

In this chapter, you will become a modeler of discerning taste. You'll learn how to identify promising model orders from the data itself, then, once the most promising models have been trained, you'll learn how to choose the best model from this fitted selection. You'll also learn a great framework for structuring your time series projects.

Exercise 1: Introduction à l’ACF et à la PACF Exercise 2: AR ou MA

Exercice actuel

Exercise 3: Ordre des tremblements de terre Exercise 4: Introduction à l’AIC et au BIC Exercise 5: Recherche de l’ordre du modèle Exercise 6: Choisir l’ordre avec l’AIC et le BIC Exercise 7: AIC et BIC vs ACF et PACF Exercise 8: Diagnostic du modèle Exercise 9: Erreur absolue moyenne Exercise 10: Statistiques récapitulatives de diagnostic Exercise 11: Tracer les diagnostics Exercise 12: Méthode de Box-Jenkins Exercise 13: Identification Exercise 14: Identification II Exercise 15: Estimation Exercise 16: Diagnostics

In this final chapter, you'll learn how to use seasonal ARIMA models to fit more complex data. You'll learn how to decompose this data into seasonal and non-seasonal parts and then you'll get the chance to utilize all your ARIMA tools on one last global forecast challenge.

Exercise 1: Seasonal time series Exercise 2: Seasonal decompose Exercise 3: Seasonal ACF and PACF Exercise 4: SARIMA models Exercise 5: Fitting SARIMA models Exercise 6: Choosing SARIMA order Exercise 7: SARIMA vs ARIMA forecasts Exercise 8: Automation and saving Exercise 9: Automated model selection Exercise 10: Saving and updating models Exercise 11: SARIMA and Box-Jenkins Exercise 12: Multiplicative vs additive seasonality Exercise 13: SARIMA model diagnostics Exercise 14: SARIMA forecast Exercise 15: Congratulations!