Moving averages range 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.
In »Memory Structures in Neural Networks« and »Regression Forecasts for Anticipatory Memory Structures« we discuss other side effects of these smoothing algorithms: They can all be seen as electronic memory structures providing specific, varying reactions to strong and weak impressions. Moving averages are also a valuable tool for data mining tasks – as reliable as they are easy to calculate. Last but not least, they can be employed for less sophisticated forecasting problems.