A Divide-and-Rule Combined Learning Method for Truly Multivariate Time Series Prediction
Bing Wei, Shiqing Sang, Liangyong Yao, Lei Gao, Yan Liu, Tao Han, Jintao Li · International Journal of Pattern Recognition and Artificial Intelligence · 2024
Multivariate time series prediction is a significant research area that aims to forecast future values based on past observations. Deep learning models with attention mechanisms have shown good predictive performance by emphasizing optimal-related sequences in the target series. However, these models ignore mutation information of nontarget sequences and the long short-term dependencies. To this end, a divide-and-rule combined learning method is proposed to address these limitations, which uses differentiated feature extractors to process different implicit features. First, we design a spatial and temporal information extractor to extract the time-dimensional feature information in the separation stage. Then, a multivariate mutation information extractor is constructed by convolution and maximum pooling layer to capture mutation information of nontarget sequences. Subsequently, the decoder component of the encoder-decoder model extracts long short-term dependencies while preserving the information of the target sequence to be predicted. Finally, in the cooperation stage, a feature fusion method based on a point attention mechanism is proposed, which can assign individual weights to each feature point and enhance the ability to focus on local areas. Experimental results on five real datasets in different domains show that the proposed method has better predictive performance compared to other baseline models.