A Novel ELM Ensemble for Time Series Prediction

Autor:	Karl Ratner, Zhen Li, Kaj-Mikael Björk, Edward R. Ratner, Kallin Khan, Amaury Lendasse
Rok vydání:	2019
Předmět:	Rest (physics) Training set Series (mathematics) Computer science business.industry Artificial intelligence Time series business Machine learning computer.software_genre computer
Zdroj:	Proceedings in Adaptation, Learning and Optimization ISBN: 9783030233068 ELM
Popis:	This paper presents a novel methodology for time series prediction. It is based on Extreme Learning Machines and an adaptive ensemble techniques. It is tested successfully on the CIF 2016 competition datasets which are composed of 72 time series in total. Among those, 48 time series are artificial with each having 108 training data points and 12 testing points. So for each artificial time series, there are 120 values, which is more than that of the rest 24 real time series.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::21edaf5474faed075b0e2c3885890110 https://doi.org/10.1007/978-3-030-23307-5_31 Zobrazit plný text záznamu