Probabilistic Forecasting of Geomagnetic Indices Using Gaussian Process Models
| Autor: | Mandar Chandorkar, Enrico Camporeale |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
010504 meteorology & atmospheric sciences
Series (mathematics) Computer science Probabilistic logic Order (ring theory) 01 natural sciences Geomagnetic index symbols.namesake Earth's magnetic field 0103 physical sciences symbols Probabilistic forecasting 010303 astronomy & astrophysics Gaussian process Algorithm 0105 earth and related environmental sciences |
| Popis: | In this chapter, we give the reader an in-depth view into building of probabilistic forecasting models for geomagnetic time series using the Gaussian process methodology outlined in the previous chapters. We highlight design decisions and practical issues that must be addressed in order to use Gaussian process models for probabilistic prediction of a quantity of interest. As a pedagogical example, we formulate, train, and test a family of Gaussian process auto-regressive models for 1-h ahead prediction of the Dst geomagnetic index. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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