Modeling time series with exponential smoothing methods
Exponential smoothing methods are one of the two families of classical forecasting models. Their underlying idea is that forecasts are simply weighted averages of past observations. When calculating those averages, more emphasis is put on the recent observations. To achieve that, the weights are decaying exponentially with time. These models are suitable for non-stationary data, that is, data with a trend and/or seasonality. Smoothing methods are popular because they are fast (not a lot of computations are required) and relatively reliable when it comes to forecasts’ accuracy.
Collectively, the exponential smoothing methods can be defined in terms of the ETS framework (Error, Trend, and Season), as they combine the underlying components in the smoothing calculations. As in the case of the seasonal decomposition, those terms can be combined additively, multiplicatively, or simply left out of the model.
Please see Forecasting...
