Skip to yearly menu bar Skip to main content


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

Gregory Benton · Wesley Maddox · Andrew Wilson

Hall E #736

Keywords: [ APP: Time Series ] [ PM: Gaussian Processes ]


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.

Chat is not available.