Exponentially Weighted Moving Average implementation with thread safety guarantees. Users of this class should
periodically record values on the structure and are free to read the current average at any time. This
implementation does not make any assumptions regarding the time between recordings and leaves the responsibility of
deciding when to record values to the user.
The only required parameter is the weighting factor, which effectively determines how quickly the moving average
will adapt to new data. The weighting factor is a number between 0 and 1 (exclusive). The closer the weighting
factor is to 0, the more influence new values will have over the moving average and thus the moving average is more
responsive; conversely, if the weighting factor is closer to 1, the moving average will take more iterations to
adapt to new data.
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defadd(value: Double): Unit
Adds the provided value to the exponentially weighted moving average.
final defasInstanceOf[T0]: T0
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defaverage(): Double
Returns the current exponentially weighted moving average.
Exponentially Weighted Moving Average implementation with thread safety guarantees. Users of this class should periodically record values on the structure and are free to read the current average at any time. This implementation does not make any assumptions regarding the time between recordings and leaves the responsibility of deciding when to record values to the user.
The only required parameter is the weighting factor, which effectively determines how quickly the moving average will adapt to new data. The weighting factor is a number between 0 and 1 (exclusive). The closer the weighting factor is to 0, the more influence new values will have over the moving average and thus the moving average is more responsive; conversely, if the weighting factor is closer to 1, the moving average will take more iterations to adapt to new data.