.png){kind=icon}
.png){kind=icon}
Return
SGCast: A New Forecasting Framework for Multilocation Geomagnetic Data With Missing Traces Based on Matrix Factorization
.png){kind=icon}
PRE
AI
DOI：10.1109/TIM.2021.3088459.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Geomagnetic data forecasting plays a critical role for natural disaster institutions to respond promptly or make decisions for the magnetic storm warning, earthquake early warning, and so on. However, the forecasting accuracy is not always reliable due to the spatial correlations among different sites and the temporal correlations from each site measurement update. To address the correlation problems, a novel sparse geomagnetic data forecasting (SGCast) matrix framework is proposed in this study. To be specific, a coupled matrix factorization is proposed to model the sparse multilocation geomagnetic data and spatial correlation, which is demonstrated with an example from seven Chinese cities. After the factorization, two subspaces, location subspace, and temporal subspace are derived. Finally, the future geomagnetic signals are reconstructed based on forecasting temporal subspace and matrix reconstruction. The experimental results from the extensive comparison studies demonstrate the superiority of the proposed SGCast approach compared to the state-of-the-art approaches, with an approximate improvement of the forecasting accuracy as 10%similar to 5%.
Keywords:
Forecasting
geomagnetic data fusion
matrix factorization
multilocation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.9
Papers:
1.9W
Citations:
5.8W
Organization
View more organizations