ARMA-voorspelling uitrollen

Nu ga je het model gebruiken dat je in de vorige oefening trainde, arma, om de absolute waarde van de Amazon-aandelengegevens te voorspellen. Onthoud dat het soms genoeg is om het verschil te voorspellen — gaan de aandelen omhoog of omlaag — maar soms is de absolute waarde cruciaal.

Het results-object van het model dat je in de vorige oefening trainde is in je omgeving beschikbaar als arma_results. De functie np.cumsum() en de oorspronkelijke DataFrame amazon zijn ook beschikbaar.

Deze oefening maakt deel uit van de cursus

ARIMA-modellen in Python

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Oefeninstructies

Gebruik de methode .get_forecast() van het object arima_results en selecteer het voorspelde gemiddelde van de volgende 10 verschillen.
Gebruik de functie np.cumsum() om je verschilvoorspelling te integreren.
Tel de laatste waarde van de oorspronkelijke DataFrame erbij op om van je voorspelling een absolute waarde te maken.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Make arma forecast of next 10 differences
arma_diff_forecast = arma_results.____(steps=____).____

# Integrate the difference forecast
arma_int_forecast = ____

# Make absolute value forecast
arma_value_forecast = arma_int_forecast + amazon.____

# Print forecast
print(arma_value_forecast)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

ARIMA-modellen in Python

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4.9+

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: Tijdreeksmodellen fitten Exercise 2: AR- en MA-modellen fitten Exercise 3: Een ARMA-model fitten Exercise 4: Een ARMAX-model fitten Exercise 5: Voorspellen Exercise 6: One-step-ahead-voorspellingen genereren Exercise 7: One-step-ahead-voorspellingen plotten Exercise 8: Dynamische voorspellingen genereren Exercise 9: Dynamische voorspellingen plotten Exercise 10: Introductie tot ARIMA-modellen Exercise 11: Verschillen nemen en ARMA fitten Exercise 12: ARMA-voorspelling uitrollen

Huidige oefening

Exercise 13: Een ARIMA-model fitten Exercise 14: ARIMA-model kiezen

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!