Among the traditional time series forecasting models, the exponential smoothing functions are one of the most popular forecasting approaches. This approach, conceptually, is close to the moving average approach we introduced previously, as both are based on forecasting the future values of the series by averaging the past observations of the series. The main distinction between the exponential smoothing and the moving average approaches is that the first is averaging all series observations, as opposed to a subset of m observations by the latter.
Furthermore, the advance exponential smoothing functions can handle series with a trend and seasonal components. In this section, we will focus on the main exponential smoothing forecasting models:
- Simple exponential smoothing model
- Holt model
- Holt-Winters model