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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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
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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
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- val alpha: Double
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