Complex exponential smoothing (CES)
Complex exponential smoothing is a time series forecasting method that combines exponential smoothing with trend and seasonality. It is a variant of the standard exponential smoothing method, which is a simple technique for smoothing out data by using a weighted average of past observations.
In complex exponential smoothing, the forecast is based on a combination of three components: a level component, a trend component, and a seasonal component. The level component represents the overall level of the time series, the trend component represents the direction of the trend (whether it is increasing or decreasing), and the seasonal component represents the periodic fluctuations in the data.
To calculate the forecast using complex exponential smoothing, the model first estimates the level component using an exponential smoothing equation. It then estimates the trend component by taking the difference between the current and previous level estimates, and the seasonal component by dividing the current level estimate by the previous level estimate. The forecast is then calculated by adding the level, trend, and seasonal components together.
One of the benefits of complex exponential smoothing is that it can account for both trend and seasonality in the data, which makes it more accurate than simple exponential smoothing in many cases…
