7b9f77a073
* new akka module split from akka-cluster * provide sigar provisioning * fix ewma usage * resolve #16121 * see #16354
68 lines
2.5 KiB
Scala
68 lines
2.5 KiB
Scala
/**
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* Copyright (C) 2009-2014 Typesafe Inc. <http://www.typesafe.com>
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*/
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package akka.cluster.metrics
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import scala.concurrent.duration.FiniteDuration
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/**
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* The exponentially weighted moving average (EWMA) approach captures short-term
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* movements in volatility for a conditional volatility forecasting model. By virtue
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* of its alpha, or decay factor, this provides a statistical streaming data model
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* that is exponentially biased towards newer entries.
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*
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* http://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average
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*
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* An EWMA only needs the most recent forecast value to be kept, as opposed to a standard
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* moving average model.
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*
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* @param alpha decay factor, sets how quickly the exponential weighting decays for past data compared to new data,
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* see http://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average
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*
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* @param value the current exponentially weighted moving average, e.g. Y(n - 1), or,
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* the sampled value resulting from the previous smoothing iteration.
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* This value is always used as the previous EWMA to calculate the new EWMA.
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*
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*/
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@SerialVersionUID(1L)
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final case class EWMA(value: Double, alpha: Double) {
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require(0.0 <= alpha && alpha <= 1.0, "alpha must be between 0.0 and 1.0")
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/**
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* Calculates the exponentially weighted moving average for a given monitored data set.
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*
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* @param xn the new data point
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* @return a new [[akka.cluster.EWMA]] with the updated value
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*/
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def :+(xn: Double): EWMA = {
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val newValue = (alpha * xn) + (1 - alpha) * value
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if (newValue == value) this // no change
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else copy(value = newValue)
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}
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}
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object EWMA {
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/**
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* math.log(2)
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*/
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private val LogOf2 = 0.69315
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/**
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* Calculate the alpha (decay factor) used in [[akka.cluster.EWMA]]
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* from specified half-life and interval between observations.
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* Half-life is the interval over which the weights decrease by a factor of two.
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* The relevance of each data sample is halved for every passing half-life duration,
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* i.e. after 4 times the half-life, a data sample’s relevance is reduced to 6% of
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* its original relevance. The initial relevance of a data sample is given by
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* 1 – 0.5 ^ (collect-interval / half-life).
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*/
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def alpha(halfLife: FiniteDuration, collectInterval: FiniteDuration): Double = {
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val halfLifeMillis = halfLife.toMillis
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require(halfLife.toMillis > 0, "halfLife must be > 0 s")
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val decayRate = LogOf2 / halfLifeMillis
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1 - math.exp(-decayRate * collectInterval.toMillis)
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}
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}
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