Definition Exponential Smoothing Forecasting

Statistical technique for detecting significant changes in data by ignoring the fluctuations irrelevant to the purpose at hand.

Definition exponential smoothing forecasting. More recent observations given larger weights by exponential smoothing methods and the weights decrease exponentially as the observations become more distant. Adaptive exponential smoothing is an extended version of exponential smoothing. Whereas in the simple moving average the past observations are weighted equally exponential functions are used to assign exponentially decreasing weights over time. This implies that recency is an important factor in forecasting values.

The simple exponential smoothing model considers the historical values and assigns weights to these values. Exponential smoothing is a time series forecasting method for univariate data that can be extended to support data with a systematic trend or seasonal component. It is a powerful forecasting method that may be used as an alternative to the popular box jenkins arima family of methods. Simple exponential smoothing forecasts are calculated using weighted averages where the weights decrease exponentially as observations come from further in the past with the smallest weights associated with the oldest observations.

This is where exponential smoothing algorithms shine. This coefficient is allowed to fluctuate with time to reflect significant changes in the pattern of the activity or phenomenon being studied. Exponential smoothing is generally used to make short term forecasts but longer term forecasts using this technique can be quite unreliable. Exponential smoothing schemes weight past observations using exponentially decreasing weights this is a very popular scheme to produce a smoothed time series.

In exponential smoothing as opposed to in moving averages smoothing older data is given progressively less relative weight importance whereas newer data is given progressively greater weight. The procedure gives heaviest weight to more recent information and smaller weight to observations in the more distant past. Exponential smoothing forecasting in excel is based on the aaa version additive error additive trend and additive seasonality of the exponential triple smoothing ets algorithm which smoothes out minor deviations in past data trends by detecting seasonality patterns and confidence intervals. The idea is that weights are higher for recent observations.

Exponential smoothing is a rule of thumb technique for smoothing time series data using the exponential window function. It is an easily learned and easily applied procedure for making some determination based on prior assumptions by the user such as seasonality. Whereas in single moving averages the past observations are weighted equally exponential smoothing assigns exponentially decreasing weights as the observation get older. Let s look at the mathematical equations for this.