Multivariable High-Dimension Time-Series Prediction in SIoT via Adaptive Dual-Graph-Attention Encoder-Decoder With Global Bayesian Optimization

n the current intelligent era, high-dimensional multivariate time-series (HMTS) data are continuously monitored by heterogeneous devices from multiple observers in the Social Internet of Things (SIoT), which forms complicated time-series forecasting issues that must be addressed. It is an emerging paradigm that emphasizes the importance of time-series prediction methods, which play a crucial role in accurately predicting future changes, further facilitating intelligent decision-making. However, it is still challenging to design accurate time-series predictors for HMTS data to handle high-dimensional variable interactions and potential spatio-temporal correlations recorded by different observers embedded in the complex data. To solve the above dilemma, we designed the dual-graphic representation mechanism (DgRM) based on the observatory features and observed variables to simultaneously mine their correlations hidden in abundant HMTS data. Subsequently, a dual-attention mechanism is introduced into the adaptive encoder-decoder module (AEdM) to construct a novel time-series predictor named after DAG-Net. With the assistance of the global Bayesian optimization (GBO) strategy, DAG-Net obtains a preferable balance between performance and robustness. Extensive experiments on three SIoT datasets demonstrated the outstanding performance of DAG-Net, surpassing contrastive prediction methods. DAG-Net achieved preferable results in air quality, transportation, and intelligent agriculture prediction tasks, with a 10.67%, 59.67%, and 19.29% performance improvement in terms of the RMSE index. More performance analysis further verified the application prospects of the proposed predictor in practical SIoT and intelligent systems.