Abstract In the real world, the inputs of many complicated systems are time-varying functions or processes. In order to predict the outputs of these systems with high speed and accuracy, this paper proposes a time series prediction model based on the wavelet process neural network, and develops the corresponding learning algorithm based on the expansion of the orthogonal basis functions. The effectiveness of the proposed time series prediction model and its learning algorithm is proved by the Mackey--Glass time series prediction, and the comparative prediction results indicate that the proposed time series prediction model based on the wavelet process neural network seems to perform well and appears suitable for using as a good tool to predict the highly complex nonlinear time series.
Received: 06 October 2007
Revised: 29 October 2007
Accepted manuscript online:
Fund: Project supported by the National
Natural Science Foundation of China (Grant No 60572174), the
Doctoral Fund of Ministry of Education of China (Grant No
20070213072), the 111 Project (Grant No B07018), the China
Postdoctoral Science Foundation (Grant No 2
Cite this article:
Ding Gang(丁刚), Zhong Shi-Sheng(钟诗胜), and Li Yang(李洋) Time series prediction using wavelet process neural network 2008 Chin. Phys. B 17 1998
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.
