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Instantuse addon functions for the Wolfram Language
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Computes quantile regression fits over a time series, a list of numbers or a list of numeric pairs
ResourceFunction["QuantileRegression"][data,knots,probs] does quantile regression over the times series or data array data using the knots specification knots for the probabilties probs. 

ResourceFunction["QuantileRegression"][data,knots,probs,opts] does quantile regression with the options opts. 
InterpolationOrder  3  interpolation order 
Method  LinearProgramming  method for the quantile regression computations 
Make a random signal:
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Compute QuantileRegression with 5 knots for the probabilities 0.25 and 0.75:
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Here are the formulas of the obtained regression quantiles:
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Here is a plot of the original data and the obtained regression quantiles:
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Find the fraction of the data points that are under the second regression quantile:
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The obtained fraction is close to the second probability, 0.75, given to QuantileRegression.
Here is a quantile regression computation over a numerical vector:
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Here is a quantile regression computation over a time series object:
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The second argument—the knots specification—can be an integer specifying the number of knots or a list of numbers specifying the knots of the Bspline basis:
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The option InterpolationOrder specifies the polynomial order of the Bspline basis. Its values are expected to be nonnegative integers:
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QuantileRegression uses LinearProgramming. Additional parameters can be passed to LinearProgramming with the Method option:
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Here is heteroscedastic data (the variance is not constant with respect to the predictor variable):
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Find quantile regression fits:
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Plot the data and the regression quantiles:
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Note that the regression quantiles clearly outline the heteroscedastic nature of the data.
A certain contextual type of anomaly is a subset of points that have variance very different than other subsets. Using quantile regression, you can (1) evaluate the regressordependent variance for each point using the regression quantiles 0.25 and 0.75; and (2) find the points that have outlier variances.
Compute and plot the variance estimates for a signal:
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Find the lower and upper thresholds for the variance outliers:
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Find the outlier positions:
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Plot the data and the outliers found:
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Here is a financial time series:
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Do a quantile regression fit and plot it:
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Here are the errors of the fit found:
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Find anomalies’ positions in the list of fit errors:
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Plot the data, fit and anomalies:
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Get temperature data:
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Convert the time series into a list of numeric pairs:
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Compute quantile regression fits:
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Plot the data and the regression quantiles:
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Find an estimate of the conditional cumulative distribution function (CDF) at the date 20171001:
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Find outliers in the temperature data—outliers are defined as points below or above the 0.02 and 0.98 regression quantiles, respectively:
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QuantileRegression can be compared with FindFormula, Fit , LinearModelFit and NonlinearModelFit:
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Quantile regression is much more robust than linear regression. In order to demonstrate that, add a few large outliers in the data:
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Here quantile regression and linear regression are applied, as in the previous example:
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Here is a plot of the obtained curves. Note that the curve corresponding to linear regression is different and a worse fit than the one from the previous example:
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Because of the linear programming formulation for some data and knots specifications, the computations can be slow.
For most data, the quantile regression fitting for probabilities 0 and 1 produces regression quantiles that are “too far away from the data.”
Find regression quantiles for probabilities 0 and 0.5 and plot them:
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Find regression quantiles for probabilities 0.5 and 1 and plot them:
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One way to fix this is to use probabilities that are close to 0 and 1 from above and below, respectively:
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Consider the following nonlinear data:
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Make a quantile regression fit with 20 knots:
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Make a quantile regression fit with 40 knots:
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Plot the regression quantiles and the data:
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You can see that the regression quantile computed with 40 knots is “overfitted” between 0 and 8—the Bspline basis knots are too densely placed between 0 and 8.
When regression quantiles are overfitted, then the estimate of the conditional cumulative distribution function (CDF) can be problematic—the estimated CDF is not a monotonically increasing function.
Compute regression quantiles using “too many” knots:
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Plot the regression quantiles:
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Here is the estimated conditional CDF:
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For certain data, it is beneficial to rescale the predictor values, predicted values, or both before doing the quantile regression computations:
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Compute and plot regression quantiles over symmetric data:
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