The folks at Knoyd walk us through time series analysis using the Box-Jenkins method:

However, this approach is not generally recommended so we have to find something more appropriate. One option could be forecasting with the Box-Jenkins methodology. In this case, we will use the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model. In this model, we have to find optimal values for seven parameters:

- Auto Regressive Component (p)
- Integration Component (d)
- Moving Average Component (q)
- Seasonal Auto Regressive Component (P)
- Seasonal Integration Component (D)
- Seasonal Moving Average Component (Q)
- Length of Season (s)
To set these parameters properly you need to have knowledge of auto-correlation functions and partial auto-correlation functions.

Read on for a nice overview of this method, as well as the importance of making sure your time series data set is stationary.

Kevin Feasel

2018-08-30

Data Science