A TimesNet-based Real-time Forecasting System for the F10.7 Index Using DRAO and Chinese Langfang Datasets

Abstract In this paper, we construct the DRAO univariate dataset, the DRAO multivariate dataset, and the Chinese Langfang dataset. We develop and compare five deep learning models (TimesNet, iTransformer, PatchTST, N -Beats, BiGRU) and a benchmark artificial neural network (ANN) model to predict the F10.7 index. We study the impact of different feature combinations on the performance of the recommended TimesNet model. Furthermore, we develop a real-time forecasting system for the F10.7 index, incorporating both univariate and multivariate TimesNet models. During the same period, we compare F10.7 prediction performance between our system and that of four foreign institutions (British Geological Survey (BGS), SWPC, Collecte Localisation Satellites (CLS), DRAO). We conduct daily averaged and hourly resolution forecasting using the Langfang dataset. To our knowledge, we establish the first TimesNet-based framework for F10.7 prediction, advancing hourly resolution F10.7 forecasting for the first time. The main results are as follows. (1) The univariate TimesNet model achieves superior prediction performance on the first to the 27th day of forecasting, outperforming both four deep learning models and the ANN model. With the increase in the prediction days, the prediction performance of the six models all shows a downward trend. (2) The multivariate TimesNet-FIAC model, using optimal feature combinations, outperforms the univariate TimesNet-F model. (3) In short-term prediction, TimesNet-FIAC within our system surpasses four foreign institutions. On the first forecasting day, its root mean square error, mean absolute error, and mean absolute percentage error decrease by 15.06%, 18.54%, and 20.90% compared to BGS, and by 3.54%, 10.21%, and 14.94% compared to CLS. (4) On the Langfang dataset, TimesNet-F demonstrates superior generalization in daily averaged short-term forecasting, and maintains good and stable performance in hourly resolution short-term prediction.