Summary of Exponential Smoothing. Abstract

Exponential Smoothing

Summary of Exponential Smoothing (ES). Abstract

Although nobody can really look into the future, modern statistical methods, econometric models and business intelligence software go a long way in helping businesses forecast and estimate what is going to happen in the future.

The ES model uses a weighted average of past and current values, adjusting weight on current values to account for the effects of swings in the data, such as seasonality. Using an alpha term (between 0-1), you can adjust the sensitivity of the smoothing effects. ES is often used on Large Scale Statistical Forecasting problems, because it is both robust and easy to apply.

ES is a popular scheme to produce a smoothed Time Series. Whereas in Single Moving Averages the past observations are weighted equally, Exponential Smoothing assigns exponentially decreasing weights as the observation get older. In other words: recent observations are given relatively more weight in forecasting than the older observations.

In the case of moving averages, the weights assigned to the observations are the same and are equal to 1/N. In Exponential Smoothing, however, there are one or more smoothing parameters to be determined (or estimated) and these choices determine the weights assigned to the observations.

Compare also: Regression Analysis | Dynamic Regression | ARIMA | Operations Research | Game Theory

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