2022-06-28-benton22a.md

title	booktitle	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes	Proceedings of the 39th International Conference on Machine Learning	A broad class of stochastic volatility models are defined by systems of stochastic differential equations, and while these models have seen widespread success in domains such as finance and statistical climatology, they typically lack an ability to condition on historical data to produce a true posterior distribution. To address this fundamental limitation, we show how to re-cast a class of stochastic volatility models as a hierarchical Gaussian process (GP) model with specialized covariance functions. This GP model retains the inductive biases of the stochastic volatility model while providing the posterior predictive distribution given by GP inference. Within this framework, we take inspiration from well studied domains to introduce a new class of models, Volt and Magpie, that significantly outperform baselines in stock and wind speed forecasting, and naturally extend to the multitask setting.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	benton22a	0	Volatility Based Kernels and Moving Average Means for Accurate Forecasting with {G}aussian Processes	1798	1816	1798-1816	1798	false	Benton, Gregory and Maddox, Wesley and Wilson, Andrew Gordon	given family Gregory Benton given family Wesley Maddox given family Andrew Gordon Wilson	2022-06-28		Proceedings of the 39th International Conference on Machine Learning	162	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v162/benton22a/benton22a.pdf