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  3. ARIMA Models in Python

Exercise

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.

Instructions 1/3

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  • Create a forecast object for the next 136 steps - the number of months until Jan 2030.
  • Assign the .predicted_mean of the forecast to the variable mean.
  • Compute the confidence intervals and assign this DataFrame to the variable conf_int.