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.
This exercise is part of the course
ARIMA Models in Python
Exercise instructions
- Create an ARIMA(2,1,2) model, using the
ARIMA
class, passing it the Amazon stocks dataamazon
. - 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
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# 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)