Data Transforms and Smoothing

Directly integrated into the Wolfram Language's uniform architecture for handling lists of data is an array of highly optimized algorithms for transforming and smoothing datasets that can routinely involve millions of elements.

ReferenceReference

Rescale  ▪  Clip  ▪  Normalize  ▪  Standardize  ▪  Accumulate  ▪  Differences

Threshold adaptively determine a suitable threshold

MovingAverage find the simple moving average with any span

ExponentialMovingAverage find the exponential moving average with damping

MovingMedian find the moving median with any span

Interpolation find an interpolation of any order in any number of dimensions

Fit linear least-squares fit

FindFit find a constrained nonlinear fit to data

ListConvolve, ListCorrelate convolve or correlate data with any kernel

ListDeconvolve restore convolved data

CellularAutomaton apply a cellular automaton rule in any number of dimensions

Fourier, InverseFourier discrete Fourier transform and inverse

Filters »

GaussianFilter  ▪  LaplacianFilter  ▪  WienerFilter  ▪  MedianFilter  ▪  ...

Wavelet Analysis »

DiscreteWaveletTransform  ▪  WaveletThreshold  ▪  ...

Peak Analysis

FindPeaks find the positions of peaks in data

EstimatedBackground estimate a smooth background in data