Sparse Variational Contaminated Noise Gaussian Process Regression for Geomagnetic Perturbations Forecasting

Conceptos Básicos

The author presents a novel extension to the Gaussian Process framework using contaminated normal likelihood function to better account for heteroscedastic variance and outlier noise, providing shorter prediction intervals with similar coverage and accuracy.

Resumen

The content discusses the application of Sparse Variational Gaussian Process (SVGP) regression models with contaminated normal noise in forecasting geomagnetic perturbations. It highlights the challenges posed by outliers in traditional models and introduces a robust approach to improve predictive performance. The study includes simulation studies to evaluate the proposed method's effectiveness compared to other robust likelihoods, showcasing superior performance in handling extreme outliers. Additionally, real-world applications on flight delays data demonstrate the practical utility of the proposed model in predicting complex non-stationary phenomena.
The content also delves into the methodology of fitting GPR models on flight delays data, emphasizing the importance of robust models due to outliers. The comparison of different GPR models reveals that GPR-CN outperforms others in terms of NLPD and MAE metrics, indicating its effectiveness in handling extreme outliers.
Furthermore, an analysis is provided on applying GPR-CN and artificial neural network (ANN) models for predicting ground magnetic perturbations. The comparison shows that GPR-CN offers comparable coverage with reduced interval length variability compared to ANN during storm and non-storm periods.

Estadísticas

Maximum value of delay: 637 minutes.
Outliers present in flight delay data.
Summary statistics for testing period:

RMSE: GPR-CN lower than ANN.
Coverage: Comparable between models.
Interval Length (IQR): Generally lower or comparable in GPR-CN model.

Citas

"The CN distribution explicitly models outliers by assigning them to a mixture component with much larger variance."
"Our goal is to predict the maximum value of horizontal magnetic perturbation over twenty-minute intervals across twelve test stations."

Ideas clave extraídas de

Sparse Variational Contaminated Noise Gaussian Process Regression for Forecasting Geomagnetic Perturbations

by Daniel Iong,... a las arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.17570.pdf

Consultas más profundas

How does the introduction of contaminated normal noise improve predictive performance compared to traditional methods

The introduction of contaminated normal noise in Gaussian Processes (GPR) improves predictive performance by explicitly modeling outliers with a separate component in the likelihood function. Traditional methods, which assume homoscedastic Gaussian noise, are sensitive to outliers and can result in biased estimates and overly confident predictions. By using contaminated normal noise, GPR is able to better account for extreme observations that deviate from the model assumptions. This leads to more robust models that provide accurate predictions even in the presence of outliers.

What are the implications of outlier handling in Gaussian Processes regression for real-world applications beyond geomagnetic perturbations

The implications of outlier handling in Gaussian Processes regression extend beyond geomagnetic perturbations to various real-world applications where data may contain sporadic extreme values or anomalies. In fields such as finance, healthcare, and environmental monitoring, outliers can significantly impact model performance and decision-making processes. By incorporating robust observation models like contaminated normal noise into GPR, practitioners can improve the accuracy and reliability of their predictions while accounting for unexpected variations in the data. This approach enhances the resilience of predictive models against noisy or irregular data points commonly encountered in practical scenarios.

How can the findings from this study be applied to enhance predictive modeling in other fields affected by outliers

The findings from this study on handling outliers with contaminated normal noise in Gaussian Processes regression have broad applications across different fields that deal with outlier-prone datasets. For instance:

Finance: Predicting stock market fluctuations or detecting fraudulent transactions could benefit from robust modeling techniques that effectively handle outliers.
Healthcare: Forecasting patient outcomes or identifying unusual medical events could be improved by accounting for atypical data points.
Supply Chain Management: Optimizing inventory levels or predicting demand spikes requires accurate forecasting methods resilient to outlier influences.
By applying the insights gained from this study to these areas, practitioners can enhance their predictive modeling capabilities and make more informed decisions based on reliable forecasts even when faced with challenging datasets containing outliers.