Howtosignalprocessing
Economists use a simple smoothing technique called “moving average” to help determine the underlying trend in housing permits and other volatile data. A moving average smoothes a series by consolidating the monthly data points into longer units of time—namely an average of several months' data.
- What is difference between moving average and smoothed moving average?
- How do you calculate smoothed moving average?
- Which is better moving average or exponential smoothing?
- Why are moving average smoothing techniques used in forecasting?
What is difference between moving average and smoothed moving average?
Also, in a Simple Moving Average, the oldest price data are removed from the Moving Average as a new price is added to the computation. The Smoothed Moving Average uses a longer period to determine the average, assigning a weight to the price data as the average is calculated.
How do you calculate smoothed moving average?
The Smoothed Moving Average displays data for a given period of time (N). The formula for calculating this average is as follows: SMMA(i) = (SUM(i-1) – SMMA(i-1) INPUT(i))/N where the first period is a simple moving average. See also Simple Moving Average.
Which is better moving average or exponential smoothing?
For a given average age (i.e., amount of lag), the simple exponential smoothing (SES) forecast is somewhat superior to the simple moving average (SMA) forecast because it places relatively more weight on the most recent observation--i.e., it is slightly more "responsive" to changes occuring in the recent past.
Why are moving average smoothing techniques used in forecasting?
Moving Averages: Moving averages rank among the most popular techniques for the preprocessing of time series. They are used to filter random "white noise" from the data, to make the time series smoother or even to emphasize certain informational components contained in the time series.
