SARIMA forecast
In the previous exercise you confirmed that a SARIMA \((1,1,1)\) x \((0,1,1)_{12}\) model was a good fit to the CO\(_2\) time series by using diagnostic checking.
Now its time to put this model into practice to make future forecasts. Climate scientists tell us that we have until 2030 to drastically reduce our CO\(_2\) emissions or we will face major societal challenges.
In this exercise, you will forecast the CO\(_2\) time series up to the year 2030 to find the CO\(_2\) levels if we continue emitting as usual.
The trained model results object is available in your environment as results.
Cet exercice fait partie du cours
ARIMA Models in Python
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Create forecast object
forecast_object = results.____
# Extract predicted mean attribute
mean = ____
# Calculate the confidence intervals
conf_int = ____
# Extract the forecast dates
dates = mean.index