Random forests-based extreme learning machine ensemble for multi-regime time series prediction

Accurate and timely predicting values of performance parameters are currently strongly needed for important complex equipment in engineering. In time series prediction, two problems are urgent to be solved. One problem is how to achieve the accuracy, stability and efficiency together, and the other is how to handle time series with multiple regimes. To solve these two problems, random forests-based extreme learning machine ensemble model and a novel multi-regime approach are proposed respectively, and these two approaches can be integrated to achieve better performance. First, the extreme learning machine (ELM) is used in the proposed model because of its efficiency. Then the regularized ELM and ensemble learning strategy are used to improve generalization performance and prediction accuracy. The bootstrap sampling technique is used to generate training sample sets for multiple base-level ELM models, and then the random forests (RF) model is used as the combiner to aggregate these ELM models to achieve more accurate and stable performance. Next, based on the specific properties of turbofan engine time series, a multi-regime approach is proposed to handle it. Regimes are first separated, then the proposed RF-based ELM ensemble model is used to learn models of all regimes, individually, and last, all the learned regime models are aggregated to predict performance parameter at the future timestamp. The proposed RF-based ELM ensemble model and multi-regime approaches are evaluated by using NN3 time series and NASA turbofan engine time series, and then the proposed model is applied to the exhaust gas temperature prediction of CFM engine. The results demonstrate that the proposed RF-based ELM ensemble model and multi-regime approach can be accurate, stable and efficient in predicting multi-regime time series, and it can be robust against overfitting.