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 a Vector or list. Each value represents an individual observation.

The second parameter lambda is the smoothing constant, which can be any real number between 0 and 1.

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 data 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.