Public Implementation of Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes

by Gregory Benton, Wesley Maddox, and Andrew Gordon Wilson.

Please cite our work if you find it useful:

@inproceedings{benton2022volatility,
  title={Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes},
  author={Benton, Gregory and Maddox, Wesley and Wilson, Andrew Gordon Gordon},
  booktitle={International Conference on Machine Learning},
  year={2022},
  organization={PMLR}
}

To see an overview of how to use Volt with synthetically generated code, see the Example notebook which walks through how the code is organized step by step.

The two core experimental settings from the paper involve modeling historical wind speeds and stock prices. The code to run these experiments with example commands is in the experiments folder.