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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 Exponential Smoothing 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
More management models
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