Smoothing algorithms are often used to remove periodic components from a data set while preserving long term trends. For example, time-series data that is sampled once a month often exhibits seasonal fluctuations. A twelve-month moving average filter will remove the seasonal component while preserving the long-term trend.
Alternatively, smoothing algorithms can be used to generate a descriptive model for exploratory data analysis. This technique is frequently used when it is impractical to specify a parameter model that describes the relationship between a set of variables.
Signal or time series smoothing techniques are used in a range of disciplines including signal processing, system identification, statistics, and econometrics.
Common smoothing algorithms include:
See also: Signal Processing Toolbox, Curve Fitting Toolbox, Econometrics Toolbox, random number, machine learning, data analysis, mathematical modeling, time series regression, kalman filter, smoothing videos