final case class EWMA(value: Double, alpha: Double) extends Product with Serializable

The exponentially weighted moving average (EWMA) approach captures short-term movements in volatility for a conditional volatility forecasting model. By virtue of its alpha, or decay factor, this provides a statistical streaming data model that is exponentially biased towards newer entries.

http://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average

An EWMA only needs the most recent forecast value to be kept, as opposed to a standard moving average model.

value

the current exponentially weighted moving average, e.g. Y(n - 1), or, the sampled value resulting from the previous smoothing iteration. This value is always used as the previous EWMA to calculate the new EWMA.

alpha

decay factor, sets how quickly the exponential weighting decays for past data compared to new data, see http://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average

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@SerialVersionUID()
Source
EWMA.scala
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Instance Constructors

  1. new EWMA(value: Double, alpha: Double)

    value

    the current exponentially weighted moving average, e.g. Y(n - 1), or, the sampled value resulting from the previous smoothing iteration. This value is always used as the previous EWMA to calculate the new EWMA.

    alpha

    decay factor, sets how quickly the exponential weighting decays for past data compared to new data, see http://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average

Value Members

  1. def :+(xn: Double): EWMA

    Calculates the exponentially weighted moving average for a given monitored data set.

    Calculates the exponentially weighted moving average for a given monitored data set.

    xn

    the new data point

    returns

    a new EWMA with the updated value

  2. val alpha: Double
  3. val value: Double