Exponential Smoothing

Exponential Smoothing Model (ESM) is a technique that can be applied to time series data, either to produce smoothed data for presentation, or to make forecasts. The time series data themselves are a sequence of observations. The observed phenomenon may be an essentially random process, or it may be an orderly, but noisy, process. Whereas in the simple moving average the past observations are weighted equally, Exponential Smoothing assigns exponentially decreasing weights over time.

Exponential Smoothing is commonly applied to financial market and economic data, but it can be used with any discrete set of repeated measurements. The raw data sequence is often represented by {xt}, and the output of the Exponential Smoothing algorithm is commonly written as {st}, which may be regarded as a best estimate of what the next value of x will be. When the sequence of observations begins at time t = 0, the simplest form of Exponential Smoothing is given by the formula below, where where α is the Smoothing Factor.

exponential smoothing

ESM is a popular scheme to create a smoothed Time Series. Whereas in Single Moving Averages the past observations are weighted equally, Exponential Smoothing assigns exponentially decreasing weights if the observation gets older. In other words: recent observations are given 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 which must be determined or estimated. These choices determine the weights assigned to the observations.

Business frameworks like Exponential Smoothing are invaluable to evaluating and analyzing various business problems. You can download business frameworks developed by management consultants and other business professionals at Flevy here.

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