Adattare modelli SARIMA

Adattare modelli SARIMA è l'inizio della fine di questo viaggio nel modeling delle serie temporali.

È importante che tu prenda familiarità con gli ordini del modello SARIMA, ed è proprio su questo che ti concentrerai qui.

In questo esercizio, farai pratica ad adattare diversi modelli SARIMA a un insieme di serie temporali.

I DataFrame di serie temporali df1, df2 e df3 sono disponibili nel tuo ambiente.

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Modelli ARIMA in Python

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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: Intro to ACF and PACF Exercise 2: AR or MA Exercise 3: Order of earthquakes Exercise 4: Intro to AIC and BIC Exercise 5: Searching over model order Exercise 6: Choosing order with AIC and BIC Exercise 7: AIC and BIC vs ACF and PACF Exercise 8: Model diagnostics Exercise 9: Mean absolute error Exercise 10: Diagnostic summary statistics Exercise 11: Plot diagnostics Exercise 12: Box-Jenkins method 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: Serie temporali stagionali Exercise 2: Decomposizione stagionale Exercise 3: ACF e PACF stagionali Exercise 4: Modelli SARIMA Exercise 5: Adattare modelli SARIMA

Esercizio in corso

Exercise 6: Scelta dell'ordine SARIMA Exercise 7: Previsioni SARIMA vs ARIMA Exercise 8: Automazione e salvataggio Exercise 9: Selezione automatica del modello Exercise 10: Salvataggio e aggiornamento dei modelli Exercise 11: SARIMA e Box-Jenkins Exercise 12: Stagionalità moltiplicativa vs additiva Exercise 13: Diagnostica del modello SARIMA Exercise 14: Previsione SARIMA Exercise 15: Congratulazioni!