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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.

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

MovingMap — map a function over a moving window with any span

ArrayFilter — map a function over a moving window in an array of any depth

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 ▪ ...

Outliers & Missing Data

DeleteAnomalies — learn from data to delete anomalous elements

SynthesizeMissingValues — fill in missing values by imputing from existing data

DeleteMissing ▪ Missing

Peak Analysis

FindPeaks — find the positions of peaks in data

EstimatedBackground — estimate a smooth background in data

Recurrence Analysis

FindRepeat ▪ FindTransientRepeat

Related Tech Notes

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Curve Fitting

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Approximate Functions and Interpolation

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Convolutions and Correlations

Related Guides

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Time Series

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Fitting Data

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Statistical Model Analysis

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Signal Processing

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Statistical Data Analysis

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Machine Learning

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Integral Transforms

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Geometric Transforms