Smoothing and Differentiation of Data by Simplified Least Squares Procedures¶
Savitzky, A., & Golay, M. J. E. (1964). Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 36(8), 1627-1639.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Domain-specific¶
- Smoothing
- Compared with a plain moving average, it can preserve polynomial features and also estimate derivatives, but window length and polynomial degree still impose a scale.
This sourcePrimary source for local-polynomial convolution coefficients used for smoothing and differentiation.
- Compared with a plain moving average, it can preserve polynomial features and also estimate derivatives, but window length and polynomial degree still impose a scale.
Mechanisms¶
- First-Difference or Derivative Estimate
- The guarding discipline is to smooth before differentiating, for example with a Savitzky–Golay derivative that fits a local polynomial rather than subtracting raw points
This sourceComputes smoothed derivatives by fitting local least-squares polynomials instead of directly differencing raw observations.
- The guarding discipline is to smooth before differentiating, for example with a Savitzky–Golay derivative that fits a local polynomial rather than subtracting raw points
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