Description
The course is designed for those who have a basic knowledge of forecasting (e.g., smoothing and regression) who wish to take both diagnostic and time series forecasting approaches to the next level.
Learning objectives
- The underlying assumptions of ARIMA models
- The main characteristics of autoregressive and moving average models as used in ARIMA
- The process of the Box-Jenkins methodology for identifying suitable ARIMA models
- How to apply the Box Jenkins methodology to both non-seasonal and seasonal data sets
- To understand when ARIMA models may be appropriate in practice