A novel double deep ELMs ensemble system for time series forecasting

Abstract

Extreme Learning Machine (ELM) has proved to be well suited to different kinds of classification and regression problems. However, failing to seek deep representation of raw data completely brought by shallow architecture has made a plenty of research work stagnant, when ELM was chosen as the basic model. Recent years, deep ELM models like Hierarchical ELM (H-ELM), deep representations learning via ELM (Dr-ELM) have been proposed to be applied in multiple applications in machine learning. In this paper, a novel double deep ELMs ensemble system (DD-ELMs-ES) is proposed to focus on the problem of time series forecasting. In the proposed system, besides H-ELM and Dr-ELM are utilized as the basic models, a novel Constrained H-ELM (CH-ELM) is presented and serves as another basic model as well. CH-ELM intends to constrain the hidden neurons’ input connection weights, so that they could be consistent with the directions of sample vectors. Whats more, a self-adaptive ReTSP-Trend pruning technique is proposed to implement ensemble pruning in DD-ELMs-ES. Benefited from the merits of combining deep learning scheme with ensemble pruning paradigm, in the empirical results, DD-ELMs-ES demonstrates better generalization performance than the basic deep ELM models and some other state-of-the-art algorithms in tackling with time series forecasting tasks.