Moving Average (Time Series) [TSMA]

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The Moving Average (Time Series) function returns the moving average of a field over a given period of time based on linear regression. Parameters ------------------ Data          The data to use in the regression. This is typically a field in a data series or a calculated value. Period        The number of bars of data to include in the regression, including the current value.                   For example, a period of 3 includes the current value and the two previous values. Function Value ------------------------ The time series moving average is calculated by fitting a linear regression line over the values for the given period, and then determining the current value for that line. A linear regression line is a straight line which is as close to all of the given values as possible. The time series moving average at the beginning of a data series is not defined until there are enough values to fill the given period. Note that a time series moving average differs greatly from other types of moving averages in that the current value follows the recent trend of the data, not an actual average of the data. Because of this, the value of this function can be greater or less than all of the values being used if the trend of the data is generally increasing or decreasing. Usage ---------- Moving averages are useful for smoothing noisy raw data, such as daily prices. Price data can vary greatly from day-to-day, obscuring whether the price is going up or down over time. By looking at the moving average of the price, a more general picture of the underlying trends can be seen. Since moving averages can be used to see trends, they can also be used to see whether data is bucking the trend. Entry/exit systems often compare data to a moving average to determine whether it is supporting a trend or starting a new one. See the sample entry/exit systems for an example of using a Moving Average in an entry/exit system. This function is the same as the Linear Regression Indicator. It is also the same as the Time Series Forecast with an offset of zero.