Generare previsioni a un passo avanti

Prevedere i prezzi azionari è molto difficile. La teoria economica classica ci dice addirittura che dovrebbe essere impossibile a causa del market clearing.

Il tuo compito in questo esercizio è provare comunque l'impossibile e prevedere il prezzo dell'azione Amazon.

In questo esercizio genererai previsioni a un passo avanti per il prezzo dell'azione e quantificherai anche l'incertezza di queste previsioni.

Un modello è già stato adattato ai dati di Amazon per te. L'oggetto dei risultati di questo modello è disponibile nel tuo ambiente come results.

Questo esercizio fa parte del corso

Modelli ARIMA in Python

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Istruzioni dell'esercizio

Usa l'oggetto results per effettuare previsioni a un passo avanti sugli ultimi 30 giorni di dati e assegna il risultato a one_step_forecast.
Assegna le tue previsioni medie a mean_forecast usando uno degli attributi dell'oggetto one_step_forecast.
Estrai gli intervalli di confidenza delle tue previsioni dall'oggetto one_step_forecast e assegnali a confidence_intervals.
Stampa le tue previsioni medie.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Generate predictions
one_step_forecast = results.____(____=___)

# Extract prediction mean
mean_forecast = one_step_forecast.____

# Get confidence intervals of  predictions
confidence_intervals = one_step_forecast.____

# Select lower and upper confidence limits
lower_limits = confidence_intervals.loc[:,'lower close']
upper_limits = confidence_intervals.loc[:,'upper close']

# Print best estimate  predictions
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: Adattare modelli per serie temporali Exercise 2: Adattare modelli AR e MA Exercise 3: Adattare un modello ARMA Exercise 4: Adattare un modello ARMAX Exercise 5: Previsioni Exercise 6: Generare previsioni a un passo avanti

Esercizio in corso

Exercise 7: Rappresentare le previsioni one-step-ahead Exercise 8: Generare previsioni dinamiche Exercise 9: Rappresentare le previsioni dinamiche Exercise 10: Introduzione ai modelli ARIMA Exercise 11: Differenziazione e stima di un ARMA Exercise 12: Srotolare la previsione ARMA Exercise 13: Adattare un modello ARIMA Exercise 14: Scelta del modello ARIMA

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: 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!