Time Series Forecasting with ARIMA

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.