Scegliere l'ordine con AIC e BIC

Ora che hai effettuato una ricerca su molti ordini di modello, valuterai i risultati per trovare il miglior ordine di modello.

La lista di tuple (p,q, valore AIC, valore BIC) che hai creato nell'esercizio precedente, order_aic_bic, è disponibile nel tuo ambiente. pandas è già stato importato come pd.

Questo esercizio fa parte del corso

Modelli ARIMA in Python

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Prova a risolvere questo esercizio completando il codice di esempio.

# 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(____)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Modelli ARIMA in Python

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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: Introduzione ad ACF e PACF Exercise 2: AR o MA Exercise 3: Ordine dei terremoti Exercise 4: Introduzione ad AIC e BIC Exercise 5: Ricerca dell'ordine del modello Exercise 6: Scegliere l'ordine con AIC e BIC

Esercizio in corso

Exercise 7: AIC e BIC vs ACF e PACF Exercise 8: Diagnostica del modello Exercise 9: Errore medio assoluto Exercise 10: Statistiche riassuntive di diagnostica Exercise 11: Grafici diagnostici Exercise 12: Metodo Box-Jenkins Exercise 13: Identificazione Exercise 14: Identificazione II Exercise 15: Stima Exercise 16: Diagnostica

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!