Statistics
ExponentialSmoothing
apply exponential smoothing to a data set
Calling Sequence
Parameters
Description
Options
Examples
ExponentialSmoothing(X, lambda, options)
X
-
data set
lambda
smoothing constant
options
(optional) equation(s) of the form option=value where option is one of ignore, or initial; specify options for the ExponentialSmoothing function
The ExponentialSmoothing function computes exponentially weighted moving averages for the original observations using the formula
where N is the number of elements in A and by default. This is useful for smoothing the data, thus eliminating cyclic and irregular patterns and therefore enhancing the long term trends.
The first parameter X is a single data sample - given as e.g. a Vector. Each value represents an individual observation.
The second parameter lambda is the smoothing constant, which can be any real number between 0 and 1.
For a more involved implementation of exponential smoothing, see TimeSeriesAnalysis[ExponentialSmoothingModel].
The options argument can contain one or more of the options shown below. These options are described in more detail in the Statistics[Mean] help page.
ignore=truefalse -- This option is used to specify how to handle non-numeric data. If ignore is set to true all non-numeric items in X will be ignored.
initial=deduce, or realcons -- This option is used to specify the initial value for the smoothed observations. By default, the first of the original observations is taken as the initial value.
See Also
Statistics[DataSmoothing]
Statistics[LinearFilter]
Statistics[MovingAverage]
TimeSeriesAnalysis
TimeSeriesAnalysis[ExponentialSmoothingModel]
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