Recurrent Kernel Online Sequential Extreme Learning Machine with Kernel Adaptive Filter for Time Series Prediction

This paper proposes a novel recurrent multi-stepsprediction model call Recurrent Kernel Online Sequential Extreme Learning Machine with Surprise Criterion (SC-RKOSELM). This model combines the strengths of Kernel Online Sequential Extreme Learning Machine (KOS-ELM), the characteristics of surprise criterion and advantages of recurrent multisteps-prediction algorithm to unleash the restriction of prediction horizon and reduce the computation complexation of the learning part. In the experiment, we employ two synthetic and two realworld data sets, including Mackey-Glass, Lorenz, palm oil price and water level in Thailand, to evaluate Recurrent Online Sequential Extreme Learning Machine (ROS-ELM) and Recurrent Kernel Online Sequential Extreme Learning Machine with Fixedbudget Criterion (FB-RKOS-ELM). The results of experiments indicate that SC-RKOS-ELM has the superior predicting ability in all data sets than others.