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It is an easily learned and easily applied procedure for making some determination based on prior assumptions by the user, such as seasonality.

Exponential smoothing is used for analysis of financial time-series data as well as the field of signal processing.

In addition to this disadvantage, if the data from each stage of the averaging is not available for analysis, it may be difficult if not impossible to reconstruct a changing signal accurately (because older samples may be given less weight).

If the number of stages missed is known however, the weighting of values in the average can be adjusted to give equal weight to all missed samples to avoid this issue.

Technically it can also be classified as an autoregressive integrated moving average (ARIMA) (0,1,1) model with no constant term..

For example, if the data were all the same except for one high data point, the peak in the "smoothed" data would appear half a window length later than when it actually occurred.

Where the phase of the result is important, this can be simply corrected by shifting the resulting series back by half the window length.

Alternatively, a statistical technique may be used to optimize the value of α.

For example, the method of least squares might be used to determine the value of α for which the sum of the quantities Unlike some other smoothing methods, such as the simple moving average, this technique does not require any minimum number of observations to be made before it begins to produce results.

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However, as long as the time series contains at least k values, this has no effect on forecasts of future values.

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