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

被引：1

作者：

Wang, Hongbo ^{[1
]}

Song, Peng ^{[1
]}

Wang, Chengyao ^{[1
]}

Tu, Xuyan ^{[1
]}

机构：

[1] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Dept Comp Sci & Technol, Beijing, Peoples R China

来源：

PROCEEDINGS OF ELM-2016 | 2018年 / 9卷

基金：

中国国家自然科学基金;

关键词：

Extreme learning machine (ELM); Time series prediction; Machine learning; NETWORKS;

D O I：

10.1007/978-3-319-57421-9_10

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：113 / 127

页数：15

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