GVA-BLS: Gaussian Vector Autoregression Broad Learning System Based on Randomly Distributed Embedding for Multistep-Ahead Prediction.

Abstract

As a model proficient in time series prediction, the recurrent broad learning system (RBLS) combines the advantages of traditional BLS and recurrent neural networks (RNNs). This combination enhances the learning of transient data and ensures computational efficiency. However, RBLS still suffers from the shortcomings of traditional RNNs. These include excessive connection weights, an unexplainable model structure, and low computational efficiency. To improve recurrent neurons and structure in RBLS, this article proposes a Gaussian vector autoregression BLS (GVA-BLS) model based on nonlinear vector autoregression and RBLS theory. GVA-BLS is applied to multistep-ahead time series prediction. Compared to traditional RBLS, GVA-BLS has three advantages: 1) GVA-BLS addresses the issue of excessive recurrent weights in RBLS enhancement nodes and the difficulty in random initialization of hyperparameters; 2) the GVA nodes use a Gaussian kernel function to perform a nonlinear transformation on the output of feature nodes, which makes the GVA-BLS more efficient; and 3) compared to the traditional enhancement nodes of RBLS, GVA nodes can map the output of feature nodes into a higher dimensional space, making GVA-BLS more suitable for short-term, high-dimensional, multistep-ahead prediction tasks. Experimental results demonstrate that GVA-BLS outperforms several recently proposed gate-structured RNNs and BLS-based models in terms of multistep-ahead prediction performance. It also reduces the computational overhead of RBLS and makes it more efficient and intuitive.