Table of Contents

Seasonal ARIMA Models

In this final chapter, you'll learn how to use seasonal ARIMA models to fit more complex data. You'll learn how to decompose this data into seasonal and non-seasonal parts and then you'll get the chance to utilize all your ARIMA tools on one last global forecast challenge. This is the Summary of lecture "ARIMA Models in Python", via datacamp.

Seasonal time series

Seasonal decompose

You can think of a time series as being composed of trend, seasonal and residual components. This can be a good way to think about the data when you go about modeling it. If you know the period of the time series you can decompose it into these components.

In this exercise you will decompose a time series showing the monthly milk production per cow in the USA. This will give you a clearer picture of the trend and the seasonal cycle. Since the data is monthly you will guess that the seasonality might be 12 time periods, however this won't always be the case.

first with an updated but still not stationary dataset:

test with an updated and made stationary dataset:

Seasonal ACF and PACF

In this exercise you will use the ACF and PACF to test this data for seasonality. You can see from the plot above that the time series isn't stationary, so you should probably detrend it. You will detrend it by subtracting the moving average. Remember that you could use a window size of any value bigger than the likely period.