Basic time-series forecasting methods (such as moving average,

weighted moving average, and simple and variable exponential smoothing) are supported by most packages.

The equally

weighted moving average approach, the more straightforward of the two, calculates a given portfolio's variance (and thus, standard deviation) using a fixed amount of historical data.

Roberts[7] is often credited for first suggesting the use of an exponential

weighted moving average, EWMA, as a statistic for a control chart (although he used the term geometric moving average).

Other authors suggest that for this purpose the usage of the exponentially

weighted moving average (EWMA) chart would be appropriate (Khoo & Quah, 2002; Takahashi, 2003).

The

weighted moving average models focus on the trends and seasonal behavior of the data.

The Exponential

Weighted Moving Average calculates the average call volume over a specific time period and then bases its projections on a formula that assigns more weight to recent activity.

For example, by using the Exponentially

Weighted Moving Average (EWMA) detection algorithm, the factors measured were timeliness, sensitivity, and specificity of chief complaints classified by CoCo for predicting outbreaks of pediatric respiratory and gastrointestinal illness (Ivanov O, Gesteland P, Hogan W, Mundorff MB, Wagner MM.

Weighted moving averages can show cyclical time series' behaviour.

Exponential smoothing uses

weighted moving averages in which only one weight--the weight for the most recent observation--is selected.

You also can use cost-of-process shifts, autocorrelation functions, and exponentially

weighted moving averages.