smoothing -凯发k8网页登录

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:

• lowess and loess: nonparametric smoothing methods using local regression models
• kernel smoothing: nonparametric approach to modeling a smooth distribution function
• smoothing splines: nonparametric approach for curve fitting
• autoregressive moving average (arma) filter: filter used when data exhibits serial autocorrelation
• hodrick-prescott filter: filter used to smooth econometric time series by extracting the seasonal components
• savitzky–golay smoothing filter: filter used when a signal has high frequency information that should be retained
• butterworth filter: filter used in signal processing to remove high frequency noise

for more information on smoothing, please see statistics and machine learning toolbox™, curve fitting toolbox™, econometrics toolbox™, system identification toolbox™, and signal processing toolbox™.