There are several methods which can be used to smooth time series data by moving averages. They are the Single Moving Average and the Double Moving Average methods. Both of them use several past data points to forecast the future.

Single Moving Average

Single Moving Average method uses the last t periods to create a forecast. The new average value is calculated by removing the oldest value and replacing it with the newest value. This method is suitable for stationary data and for data which does not contain trend or seasonal components.

Let us have N points of data and use T observations to calculate the average value, notated as MA(T). It is described as:

Time	Moving Average	Forecast
T	Y =
T+1	Y =
T+2	Y =
	…..

Double Moving Average

This method is based on the calculation of the second moving average. The second moving average is calculated from the average of first moving average, notated by MA (T x T), means MA (T) period from MA (T) period. This method can be used to forecast data with a linear trend component.

The procedure to calculate double moving average is:

1. Calculate single moving average
2. Calculate adjustment, which is the difference between single-MA and double-MA , where
3. Adjust trend from period n to n+m, if you want to forecast m period ahead.

The forecasting value for m period ahead is a_n – where it is the adjusted average value for period n – added by the value of multiplication between m and trend component b_n.