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01-03-2014 | Methodologies and Application

Financial time series prediction by a random data-time effective RBF neural network

Authors: Hongli Niu, Jun Wang

Published in: Soft Computing | Issue 3/2014

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Abstract

An improved neural network of time series predicting is presented in this paper. We introduce a random data-time effective radial basis function neural network in determination of the output weights, the center vectors and the widths in the hidden layer of the network. In the training modeling, we consider that the historical data on the financial market is key to the investors’ decision-making for their investing positions, and the impact of historical data depends closely on the time. We develop a random data-time effective function to describe this impact strength, and a weight is given to each of the historical data, where a drift function and a random Brownian volatility function are applied to express the behavior of the time strength. Further, this neural network is applied to the prediction of financial price series of crude oil, SSE, N225 and DAX. The empirical experiments show that the proposed neural network results in better performance in financial time series forecasting and is advantageous in increasing the forecasting precision.

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Title: Financial time series prediction by a random data-time effective RBF neural network
Authors: Hongli Niu
Jun Wang
Publication date: 01-03-2014
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 3/2014
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-013-1070-2

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