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Smoothing of a noisy sine (blue curve) with a moving average (red curve). In statistics, a moving average (rolling average or running average or moving mean [1] or rolling mean) is a calculation to analyze data points by creating a series of averages of different selections of the full data set.
That’s different from annual return, which simply measures the return a security generates within a given 12-month period. It’s also different from yield . How to Calculate Rolling Returns
YTD measures are more sensitive to changes early in the year than later in the year. In contrast, measures like the 12-month ending (or year-ending) are less affected by seasonal influences. For example, to calculate year-to-date invoicing for a company, sum the invoice totals for each month of the current year up to the present date. [2]
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A simple moving average can be considered to be a sequence of temporal means over periods of equal duration. (If the time variable is continuous, the average value during the time period is the integral over the period divided by the length of the duration of the period.) [1]
In the statistical analysis of time series, autoregressive–moving-average (ARMA) models are a way to describe a (weakly) stationary stochastic process using autoregression (AR) and a moving average (MA), each with a polynomial. They are a tool for understanding a series and predicting future values.
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In time series analysis, the moving-average model (MA model), also known as moving-average process, is a common approach for modeling univariate time series. [ 1 ] [ 2 ] The moving-average model specifies that the output variable is cross-correlated with a non-identical to itself random-variable.