Time Series Moving Average   

The time series moving average is calculated using linear regression techniques. Rather than plotting a straight linear regression line, a time series moving average plots the last point of the line. The Moving Average (Time Series) function returns the moving average of a field over a given period of time based on linear regression.
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.
It does this using the specified number of periods.  The individual points are then connected together with a line to form a time series moving average. This moving average is sometimes referred to as a “moving linear regression” study or a “regression oscillator.”
Usage:

Moving averages are useful for smoothing noisy raw data. 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.