# Simultaneous Multivariate Forecast of Space Weather Indices using Deep Neural Network Ensembles

arXiv: https://arxiv.org/abs/2112.09051 · HTML: https://mlsanity.com/paper/2112.09051/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Simple and Effective VAE Training with Calibrated Decoders](https://mlsanity.com/paper/2006.13202/) (2020)
- [A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission](https://mlsanity.com/paper/1903.04538/) (2019)
- [An Introductory Study on Time Series Modeling and Forecasting](https://mlsanity.com/paper/1302.6613/) (2013)
- [Simultaneously forecasting global geomagnetic activity using Recurrent Networks](https://mlsanity.com/paper/2010.06487/) (2020)

## Built on by (most cited first, 2 of 2)

- [Machine Learning in Orbit Estimation: a Survey](https://mlsanity.com/paper/2207.08993/) (2022)
- [Probabilistic Solar Proxy Forecasting with Neural Network Ensembles](https://mlsanity.com/paper/2306.02169/) (2023)

## Abstract (arXiv)

Solar radio flux along with geomagnetic indices are important indicators of solar activity and its effects. Extreme solar events such as flares and geomagnetic storms can negatively affect the space environment including satellites in low-Earth orbit. Therefore, forecasting these space weather indices is of great importance in space operations and science. In this study, we propose a model based on long short-term memory neural networks to learn the distribution of time series data with the capability to provide a simultaneous multivariate 27-day forecast of the space weather indices using time series as well as solar image data. We show a 30-40% improvement of the root mean-square error while including solar image data with time series data compared to using time series data alone. Simple baselines such as a persistence and running average forecasts are also compared with the trained deep neural network models. We also quantify the uncertainty in our prediction using a model ensemble.