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Statistics[ExponentialSmoothing] - apply exponential smoothing to a data set
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Calling Sequence
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ExponentialSmoothing(X, lambda, options)
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Parameters
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X
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data set
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lambda
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smoothing constant
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options
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(optional) equation(s) of the form option=value where option is one of ignore, or initial; specify options for the ExponentialSmoothing function
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Description
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The ExponentialSmoothing function computes exponentially weighted moving averages for the original observations using the formula
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![S[i+1] = lambda*A[i+1]+(1-lambda)*S[i], i = (1*`..`)*N-1](/support/helpjp/helpview.aspx?si=6762/file02865/math76.png)
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where N is the number of elements in A and ![S[1] = A[1]](/support/helpjp/helpview.aspx?si=6762/file02865/math83.png) by default. This is useful for smoothing the data, thus eliminating cyclic and irregular patterns and therefore enhancing the long term trends.
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The first parameter X is a single data sample - given as a Vector or list. Each value represents an individual observation.
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The second parameter lambda is the smoothing constant, which can be any real number between 0 and 1.
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Options
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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.
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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 data will be ignored.
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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.
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Download Help Document
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