An optimized twin support vector regression algorithm enhanced by ensemble empirical mode decomposition and gated recurrent unit.

Despite the rapid development of support vector regression (SVR), it costs unacceptable training time in large-scale datasets and is hard to fit complex, high frequency oscillating, and non-stationary time series data. SVRs are still perplexed by the selection of critical parameters and hidden noise in input data. This work proposes a hybrid model to overcome these issues that need to be resolved, namely EEMD-GRU-TWSVRCSSA. The proposed model utilizes twin support vector regression (TWSVR) to overcome the shortcomings of the SVR in terms of training time and fitting accuracy. A novel meta -heuristic algorithm, cloud salp swarm algorithm (CSSA), is employed to automatically select the optimal hyper parameters for the TWSVR. The ensemble empirical mode decomposition (EEMD) reduces the influences of hidden noise in the input data, meanwhile splitting the high-frequency and low-frequency sub-datasets and feeding them to the gated recurrent unit (GRU) and TWSVR-based model, respectively. The forecasting of the proposed algorithm and other alternative algorithms are conducted on three real-world electric load datasets from the National Electricity Market (NEM), Queensland and New South Wales regions, Australia, and the well-known National Grid UK. Experimental results demonstrate the superiority and competitiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]