Short Term Prediction of Continuous Time Series Based on Extreme Learning Machine

Abstract

Extreme Learning Machine (ELM) is a popular tool of machine learning, which has been used in many fields. Time series prediction is usually a complex problem without related parameters or features. In this paper, a prediction method for continuous time series based on the theory of extreme learning machines is proposed, which focus on short term prediction of continuous time series. Firstly, the ST-ELMpredicting model is constructed. Then the ways of training and predicting is analyzed. ST-ELM uses time series and predicted value to adjust itself. Mackey-Glass and Lorenz time series have been used as example for demonstration. It is showed this method can predict continuous time series timely and accurately without related parameters or features of time series.