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# Volt
Public Implementation of
*Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes* [link]
[*Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes*](https://arxiv.org/abs/2207.06544)
by [Gregory Benton](https://g-benton.github.io/), [Wesley Maddox](https://wjmaddox.github.io), and [Andrew Gordon Wilson](https://cims.nyu.edu/~andrewgw/).
@@ -8,7 +8,7 @@ Please cite our work if you find it useful:
```
@inproceedings{benton2022volatility,
title={olatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes},
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},
@@ -24,4 +24,4 @@ To see an overview of how to use Volt with synthetically generated code, see the
## Experiments
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.
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.