Exponential smoothing methods are suitable for non-stationary data (that is, data with a trend and/or seasonality) and work similarly to exponential moving averages. The forecasts are weighted averages of past observations. These models put more emphasis on recent observations as the weights become exponentially smaller with time. Smoothing methods are popular because they are fast (not a lot of computations are required) and relatively reliable when it comes to forecasts:
Simple exponential smoothing: The most basic model is called Simple Exponential Smoothing (SES). This class of models is most apt for cases when the considered time series does not exhibit any trend or seasonality. They also work well with series with only a few data points.
The model is parameterized by a smoothing parameter α with values between...
