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.
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
Calculates the exponentially weighted moving average for a given monitored data set.
Calculates the exponentially weighted moving average for a given monitored data set.
the new data point
a new EWMA with the updated value
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
the current exponentially weighted moving average, e.g.
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.
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.
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.
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