Determining the input dimension of a neural network for nonlinear time series prediction
Zhang Sheng (张胜)ab, Liu Hong-Xing (刘红星)a, Gao Dun-Tang (高敦堂)a, Du Si-Dan (都思丹)a
a Department of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China; b Department of Physics, Nanjing Normal University, Nanjing 210097, China
Abstract Determining the input dimension of a feed-forward neural network for nonlinear time series prediction plays an important role in the modelling. The paper first summarizes the current methods for determining the input dimension of the neural network. Then inspired by the fact that the correlation dimension of a nonlinear dynamic system is the most important feature of it, the paper presents a new idea that the input dimension of the neural network for nonlinear time series prediction can be taken as an integer just greater than or equal to the correlation dimension. Finally, some validation examples and results are given.
Received: 18 November 2002
Revised: 09 February 2003
Accepted manuscript online:
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos 59905011 and 60275041).
Cite this article:
Zhang Sheng (张胜), Liu Hong-Xing (刘红星), Gao Dun-Tang (高敦堂), Du Si-Dan (都思丹) Determining the input dimension of a neural network for nonlinear time series prediction 2003 Chinese Physics 12 594
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
