ExponentialSmoothing - Maple Help
For the best experience, we recommend viewing online help using Google Chrome or Microsoft Edge.

Online Help

Statistics

  

ExponentialSmoothing

  

apply exponential smoothing to a data set

 

Calling Sequence

Parameters

Description

Options

Examples

Calling Sequence

ExponentialSmoothing(X, lambda, options)

Parameters

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

Description

• 

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].

Options

  

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.

Examples

See Also

Statistics

Statistics[DataSmoothing]

Statistics[LinearFilter]

Statistics[MovingAverage]

TimeSeriesAnalysis

TimeSeriesAnalysis[ExponentialSmoothingModel]

 


Download Help Document