Open AccessLinear Multistep F10.7 Forecasting Based on Task Correlation and Heteroscedasticity
Author(s) -
Wang Zhen,
Hu Qinghua,
Zhong Qiuzhen,
Wang Yun
Publication year - 2018
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1029/2018ea000393
Subject(s) - heteroscedasticity , econometrics , computer science , task (project management) , measure (data warehouse) , correlation , machine learning , mathematics , data mining , economics , geometry , management
Solar 10.7‐cm radio flux (F10.7) is an important measure of solar radio emission activity. Accurate F10.7 forecasting plays a key role in both space weather and global environment forecasts. We discover that forecasting errors are heteroscedastic, something that is not often considered in previous models. In addition, task correlation between different forecasting steps is ignored in current multistep‐ahead forecast models. In this work, we propose a linear multistep forecasting model based on the correlation between different forecasting steps and the characteristic of heteroscedasticity. Further, we introduce a variational Bayesian procedure to optimize the model. The performance of the proposed model is tested on F10.7 historical data. The results show that the proposed model improves the performance of multistep F10.7 forecasting by considering correlation and heteroscedasticity.