Abstract
In this paper we present a prediction method of consumer price index (CPI) based on process neural network (PNN). In order to reduce errors, after the raw data was directly expressed as a set of orthogonal basis expanded form, we made use of time-varying input function feature of process neural network and trained process neural network with combined type improved BP algorithm. We achieved a multi-variable CPI prediction with non-linear model of process neural networks gotten by above-mentioned result and illustrated the advantage of process neural network compared to traditional neural network in economic time series prediction. We provide a new method for economic time series prediction in this paper.
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© 2012 Springer-Verlag GmbH Berlin Heidelberg
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Ge, L., Yin, G. (2012). Application of Process Neural Network on Consumer Price Index Prediction. In: Luo, J. (eds) Affective Computing and Intelligent Interaction. Advances in Intelligent and Soft Computing, vol 137. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27866-2_51
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DOI: https://doi.org/10.1007/978-3-642-27866-2_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27865-5
Online ISBN: 978-3-642-27866-2
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