Fusion Network Model Based on Broad Learning System for Multidimensional Time-Series Forecasting

Multidimensional time-series prediction is significant in various fields, such as human production and life, weather forecasting, and artificial intelligence. However, a single model can only focus on specific features of time-series data, making it unable to consider both linear and nonlinear components simultaneously. In this study, we propose a fusion network that combines the advantages of deep and broad networks for multidimensional time-series prediction tasks. The complex multidimensional time-series data are divided into nonlinear and time-series data. Restricted Boltzmann machine and mapping functions are used for feature learning and generating mapping nodes at the mapping layer. The echo state network and gate recurrent unit are applied in the enhancement layer. The proposed model has been validated on PM2.5 and wind turbine power datasets, proving superior performance in multistep prediction tasks compared to the baseline models.