## Time Series Forecasting with ARIMA

This tutorial demonstrates how to implement the models and forecasting discussed in this unit. Since we are using Google Colab, you can jump to Step 2 to begin this programming example. Upon completing this tutorial, you should be able to construct models, make forecasts and validate forecasts given a time series data set.

### Conclusion

In this tutorial, we described how to implement a seasonal ARIMA model in Python. We made extensive use of the `pandas`

and `statsmodels`

libraries and showed how to run model diagnostics, as well as how to produce forecasts of the CO2 time series.

Here are a few other things you could try:

- Change the start date of your dynamic forecasts to see how this affects the overall quality of your forecasts.
- Try more combinations of parameters to see if you can improve the goodness-of-fit of your model.
- Select a different metric to select the best model. For example, we used the
`AIC`

measure to find the best model, but you could seek to optimize the out-of-sample mean square error instead.

For more practice, you could also try to load another time series dataset to produce your own forecasts.