Gaussian Process RBF Forecasts

Article MQL5 articles

Summary

The article introduces Gaussian processes as a statistical forecasting framework that predicts a distribution, including a mean and uncertainty, rather than only a point estimate. It focuses on the radial basis function kernel, which measures similarity between time indices according to their distance. The MQL5 implementation forms covariance matrices for past observations and future points, adds noise terms, inverts the past covariance matrix, and calculates predictive means and variances. Kernel variance and length scale are adjustable parameters that require careful tuning.

The article presents Gaussian processes as flexible for nonlinear patterns and noisy data, while noting that matrix operations make them computationally expensive compared with methods such as ARIMA. Although uncertainty estimates are described as a central advantage, the implementation and tests do not make practical use of them to guide trades. Only the RBF kernel is explored, and the article provides no quantitative evidence of trading performance or out-of-sample forecasting quality. It suggests testing combinations with other signals but leaves that validation to the reader.

Key ideas

Gaussian processes produce predictive means and uncertainty estimates using covariance relationships among observations.
The RBF kernel assigns greater similarity to nearby time points and is controlled by variance and length-scale parameters.
Forecast calculations use past, cross, and future covariance matrices to derive predicted means and variances.
Gaussian processes can model flexible nonlinear patterns but require costly matrix computations.
The article implements only the RBF kernel and does not demonstrate that uncertainty estimates improve trading results.

Gaussian Process RBF Kernels for Time-Series Forecasts

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