Concept
Gaussian Process Regression
Gaussian Process Regression, also known as GPR, is a Machine Learning concept. The Library holds 2 implementations, each one a working definition you can pull into Quant.
Top Gaussian Process Regression indicators
2 total
What is Gaussian Process Regression?
Gaussian process regression (GPR) is a Bayesian, nonparametric way to fit a smooth curve through data. A Gaussian process treats the unknown curve as a random function: any finite set of its values is jointly Gaussian, fully described by a mean function and a covariance kernel that encodes how strongly nearby points move together. Conditioning that prior on observed prices yields a posterior whose mean is the fitted curve and whose variance gives an uncertainty band at every bar, so the method outputs both an estimate and how unsure it is.
On charts, GPR is fitted to a recent window of price to draw a smooth path with confidence envelopes, sometimes extended a few bars forward. The kernel's length-scale hyperparameter controls smoothness, and a noise term controls how tightly the curve hugs the data. Beyond the observed window, with the stationary kernels commonly used, the posterior mean relaxes toward the prior and the bands widen quickly, which is the model's own admission that forward projections are scenarios, not forecasts. It differs from kernel regression chiefly by producing that uncertainty estimate.
How traders use it
- As an adaptive smoother: the posterior mean acts as a flexible fitted line through recent price, an alternative to polynomial regression that does not force a fixed curve shape.
- As uncertainty bands: price pressing the outer posterior band flags a statistically stretched move relative to the fitted curve, similar in spirit to linear-regression channel readings.
- As a short-horizon projection: the curve is extended forward with widening bands and read as a scenario envelope rather than a point prediction.
Related concepts · Learned models
Concept family
Machine Learning
32 concepts mapped · 21 in the Library
Gaussian Process Regression FAQ
How is Gaussian process regression different from kernel regression?
Kernel regression, such as the Nadaraya-Watson estimator, computes a locally weighted average of observations and returns only a point estimate. GPR is probabilistic: it places a Gaussian prior over whole functions and returns a posterior mean plus a variance, so uncertainty bands come built in. Both rely on kernels, but in GPR the kernel defines covariance between points rather than averaging weights.
Can Gaussian process regression predict future prices?
It can extrapolate, but its own math argues for caution: outside the observed window the posterior variance climbs toward its prior level and the mean drifts back toward the prior, so forward paths carry wide error bands within a few bars. Treat projections as smoothed scenarios for planning, not forecasts, and remember the fit changes whenever the window or kernel settings change.
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