Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/19952
Title: Sparsely connected neural network-based time series forecasting
Authors: Guo, ZX
Wong, WK 
Li, M
Keywords: Error measures
M3-competition
Sparsely connected neural networks
Telecommunications data
Time series
Training sample sizes
Issue Date: 2012
Publisher: Elsevier
Source: Information sciences, 2012, v. 193, p. 54-71 How to cite?
Journal: Information sciences 
Abstract: This study addresses the time series forecasting performance of sparsely connected neural networks (SCNNs). A novel type of SCNNs is presented based on the Apollonian networks. In terms of three types of publicly available benchmark data, extensive experiments were conducted to compare the forecasting performance of the proposed SCNNs, randomly connected SCNNs and traditional feed-forward neural networks. The comparison results show that the proposed networks generate the best time series forecasting performance and the traditional networks generate the worst in terms of training speed and forecasting accuracy. The performance of the proposed SCNNs is evaluated further based on different training sample sizes and training accuracy measures. The experimental results indicate that larger training sample sizes do not necessarily give better forecasts while forecasts based on training accuracy measures, MAD and MAPE are generally superior to those based on MSE and MASE.
URI: http://hdl.handle.net/10397/19952
ISSN: 0020-0255
EISSN: 1872-6291
DOI: 10.1016/j.ins.2012.01.011
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