Probabilistic forecasting of the disturbance storm time index: An autoregressive Gaussian process approach
| Autor: | Simon Wing, Enrico Camporeale, Mandar Chandorkar |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Atmospheric Science
Space weather 010504 meteorology & atmospheric sciences Probabilistic forecasting Computer science Model selection Probabilistic logic 01 natural sciences symbols.namesake Autoregressive model Kriging Machine learning 0103 physical sciences Disturbance storm time index symbols Gaussian process 010303 astronomy & astrophysics Algorithm Dst index 0105 earth and related environmental sciences |
| Zdroj: | Space Weather, 15(8), 1004-1019 |
| ISSN: | 1542-7390 |
| DOI: | 10.1002/2017sw001627 |
| Popis: | We present a methodology for generating probabilistic predictions for the Disturbance Storm Time(Dst) geomagnetic activity index. We focus on the One Step Ahead prediction task and use the OMNI hourly resolution data to build our models. Our proposed methodology is based on the technique of Gaussian Process Regression. Within this framework we develop two models; Gaussian Process Autoregressive (GP-AR) and Gaussian Process Autoregressive with eXogenous inputs (GP-ARX). We also propose a criterion to aid model selection with respect to the order of autoregressive inputs. Finally, we test the performance of the GP-AR and GP-ARX models on a set of 63 geomagnetic storms between 1998 and 2006 and illustrate sample predictions with error bars for some of these events. |
| Databáze: | OpenAIRE |
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