Orden kiezen met AIC en BIC

Nu je een zoekactie hebt uitgevoerd over veel modelordes, ga je je resultaten evalueren om de beste modelorde te vinden.

De lijst met tuples (p,q, AIC-waarde, BIC-waarde) die je in de vorige oefening hebt gemaakt, order_aic_bic, is beschikbaar in je omgeving. pandas is ook geïmporteerd als pd.

Deze oefening maakt deel uit van de cursus

ARIMA-modellen in Python

Cursus bekijken

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Construct DataFrame from order_aic_bic
order_df = pd.DataFrame(____, 
                        columns=[____])

# Print order_df in order of increasing AIC
print(____)

# Print order_df in order of increasing BIC
print(____)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

ARIMA-modellen in Python

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Begin de cursus gratis

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: Introductie tot ACF en PACF Exercise 2: AR of MA Exercise 3: Orde van aardbevingen Exercise 4: Introductie tot AIC en BIC Exercise 5: Zoeken naar de modelorde Exercise 6: Orden kiezen met AIC en BIC

Huidige oefening

Exercise 7: AIC en BIC vs ACF en PACF Exercise 8: Modeldiagnostiek Exercise 9: Mean absolute error Exercise 10: Diagnostische samenvattingsstatistieken Exercise 11: Diagnostische plots Exercise 12: Box-Jenkins-methode Exercise 13: Identificatie Exercise 14: Identificatie II Exercise 15: Schatting Exercise 16: Diagnostiek

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!