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

• URL: http://arxiv.org/abs/2207.06544v1
• Date: Wed, 13 Jul 2022 23:02:54 GMT
• Title: Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
• Authors: Gregory Benton, Wesley J. Maddox, Andrew Gordon Wilson
• Abstract summary: We show how to re-cast a class of volatility models as a hierarchical Gaussian process (GP) model with specialized covariance functions. 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.
• Score: 36.712632126776285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A broad class of stochastic volatility models are defined by systems of stochastic differential equations. 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.

Related papers

• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• Influence Functions for Scalable Data Attribution in Diffusion Models [52.92223039302037]
  Diffusion models have led to significant advancements in generative modelling. Yet their widespread adoption poses challenges regarding data attribution and interpretability. In this paper, we aim to help address such challenges by developing an textitinfluence functions framework.
  arXiv  Detail & Related papers  (2024-10-17T17:59:02Z)
• Scaling and renormalization in high-dimensional regression [72.59731158970894]
  This paper presents a succinct derivation of the training and generalization performance of a variety of high-dimensional ridge regression models. We provide an introduction and review of recent results on these topics, aimed at readers with backgrounds in physics and deep learning.
  arXiv  Detail & Related papers  (2024-05-01T15:59:00Z)
• Fusion of Gaussian Processes Predictions with Monte Carlo Sampling [61.31380086717422]
  In science and engineering, we often work with models designed for accurate prediction of variables of interest. Recognizing that these models are approximations of reality, it becomes desirable to apply multiple models to the same data and integrate their outcomes.
  arXiv  Detail & Related papers  (2024-03-03T04:21:21Z)
• Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing Flows [28.62579476863723]
  We introduce normalizing flows to re parameterize the ODE vector field, resulting in a data-driven prior distribution. We also apply normalizing flows to the posterior inference of GP ODEs to resolve the issue of strong mean-field assumptions. We validate the effectiveness of our approach on simulated dynamical systems and real-world human motion data.
  arXiv  Detail & Related papers  (2023-09-17T09:28:47Z)
• Hierarchical Gaussian Process Models for Regression Discontinuity/Kink under Sharp and Fuzzy Designs [0.0]
  We propose nonparametric Bayesian estimators for causal inference exploiting Regression Discontinuity/Kink (RD/RK) These estimators are extended to hierarchical GP models with an intermediate Bayesian neural network layer. Monte Carlo simulations show that our estimators perform similarly and often better than competing estimators in terms of precision, coverage and interval length.
  arXiv  Detail & Related papers  (2021-10-03T04:23:56Z)
• Latent Gaussian Model Boosting [0.0]
  Tree-boosting shows excellent predictive accuracy on many data sets. We obtain increased predictive accuracy compared to existing approaches in both simulated and real-world data experiments.
  arXiv  Detail & Related papers  (2021-05-19T07:36:30Z)
• Generative Temporal Difference Learning for Infinite-Horizon Prediction [101.59882753763888]
  We introduce the $gamma$-model, a predictive model of environment dynamics with an infinite probabilistic horizon. We discuss how its training reflects an inescapable tradeoff between training-time and testing-time compounding errors.
  arXiv  Detail & Related papers  (2020-10-27T17:54:12Z)
• Recurrent Conditional Heteroskedasticity [0.0]
  We propose a new class of financial volatility models, called the REcurrent Conditional Heteroskedastic (RECH) models. In particular, we incorporate auxiliary deterministic processes, governed by recurrent neural networks, into the conditional variance of the traditional conditional heteroskedastic models.
  arXiv  Detail & Related papers  (2020-10-25T08:09:29Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)
• Transport Gaussian Processes for Regression [0.22843885788439797]
  We propose a methodology to construct processes, which include GPs, warped GPs, Student-t processes and several others. Our approach is inspired by layers-based models, where each proposed layer changes a specific property over the generated process. We validate the proposed model through experiments with real-world data.
  arXiv  Detail & Related papers  (2020-01-30T17:44:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.