What question did this study set out to answer?

The aim is to enhance financial predictions by applying Gaussian Process regression to denoise datasets.

January 27, 2026

Synthetic Stocks and Market Movement Estimation: An Analysis with Gaussian Process, Drawdown, and Data Science

Key Points

The aim is to enhance financial predictions by applying Gaussian Process regression to denoise datasets.
Implemented Gaussian Process regression to denoise financial data.
Derived predictive means and confidence intervals using a Gaussian kernel.
Created synthetic data points for improved prediction accuracy.
Calculated drawdown based on predicted means from the denoised dataset.
Applied various machine/deep learning approaches to assess the impact of data densification.
Improved detection of significant fluctuations in financial datasets.
Greater accuracy in predictions observed with shorter time intervals.
Synthetic data points led to better prediction outcomes.

Abstract

In this study, Gaussian Process regression (GPR) is implemented as a method to denoise observed financial data and to make predictions at densely interpolated time points. The methodology is applied to various empirical financial datasets. A Gaussian kernel with data-dependent initialization is implemented to derive predictive means and confidence intervals. With this procedure, synthetic data points are created from the denoised dataset for better prediction accuracy. The drawdown of the denoised dataset is calculated using the predicted mean derived by GPR, and the model for predictions is trained with those values. Finally, various machine/deep learning-based approaches are implemented to show that the data densification with GPR improves the detection of upcoming significant fluctuation of a given financial dataset. It is shown that with shorter time intervals of the data, the improvements are more significant.

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