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Fitting an ARIMA model

In this exercise you'll learn how to be lazy in time series modeling. Instead of taking the difference, modeling the difference and then integrating, you're just going to lets statsmodels do the hard work for you.

You'll repeat the same exercise that you did before, of forecasting the absolute values of the Amazon stocks dataset, but this time with an ARIMA model.

A subset of the stocks dataset is available in your environment as amazon and so is the ARIMA model class.

Este exercício faz parte do curso

ARIMA Models in Python

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Instruções do exercício

  • Create an ARIMA(2,1,2) model, using the ARIMA class, passing it the Amazon stocks data amazon.
  • Fit the model.
  • Make a forecast of mean values of the Amazon data for the next 10 time steps. Assign the result to arima_value_forecast.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Create ARIMA(2,1,2) model
arima = ____

# Fit ARIMA model
arima_results = ____

# Make ARIMA forecast of next 10 values
arima_value_forecast = ____.____(steps=____).____

# Print forecast
print(arima_value_forecast)
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