Exponential Moving Average


The Exponential Moving Average, or exponentially weighted moving average (EWMA), function computes the average of a set of input values over a specified number of periods. In this function, a greater weight is given to more recent data. This function can be used to smooth a data series, which helps to reduce noise and make it easier to spot data trends.

The mathematical formula being calculated is as follows:

EWMAt = λYt + (1 – λ)EWMAt-1 
for t = 1, 2, ..., n.

Where EWMA0 is the mean of historical data, Y is the value, n is the number of periods, and λ is the weight constant, which is set to 2 / (n +1). You can find more information from the National Institute of Standards and Technology.

Exponential Moving Average
Exponential Moving Average

1. Syntax


2. Input

The Exponential Moving Average function requires the following input:

  • d0 - The set of data values for which the Exponential Moving Average is calculated.

3. Parameters

The Exponential Moving Average function requires the following parameters:

  • s0 - The number of periods to use in the calculation. The default value is 10.
  • Alignment (Optional) – Hierarchy placeholder to be used as the alignment axis.

4. Output

The Exponential Moving Average function generates the following output:

  • Exponential Moving Average - The Exponential Moving Average result set.

5. See also

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