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Article

Financial time series prediction using exogenous series and combined neural networks

Authors:

Manoel C. Amorim Neto,

George D. C. Calvalcanti,

Tsang Ing RenAuthors Info & Claims

IJCNN'09: Proceedings of the 2009 international joint conference on Neural Networks

Pages 2578 - 2585

Published: 14 June 2009 Publication History

Abstract

Time series forecasting have been a subject of interest in several different areas of research such as: meteorology, demography, health, computer and finance. Since it can be applied to various practical problems in real world, techniques to predict time series have been a topic of increasing research activities, especially in the financial sector that has a great interest in the forecast of the stock market. In this article, we are interested in the forecast of the time series related to the Brazilian oil company, Petrobras (PETR4). A methodology based on information obtained from exogenous series was used in combination with a neural network to predict the PETR4 stock series. Exogenous series were selected by analyzing the correlation between the series with the Petrobras stocks series. In this way, the prediction was obtained by not just using the previous values of the series but also by using information external to the PETR4 series. The values of the selected series were used as features for a prediction stage based on combined neural networks. To evaluate the performance of the system classical measurements were used, however we also introduce a new performance index called Sum of the Losses and Gains (SLG).

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Cited By

Ma ZDai Q(2016)Selected an Stacking ELMs for Time Series PredictionNeural Processing Letters10.1007/s11063-016-9499-944:3(831-856)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1007/s11063-016-9499-9
Ma ZDai QLiu N(2015)Several novel evaluation measures for rank-based ensemble pruning with applications to time series predictionExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.07.04942:1(280-292)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1016/j.eswa.2014.07.049

Financial time series prediction using exogenous series and combined neural networks

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Published In

cover image Guide Proceedings

IJCNN'09: Proceedings of the 2009 international joint conference on Neural Networks

June 2009

3570 pages

ISBN:9781424435494

Sponsors

Georgia Tech: Georgia Institute of Technology
ieee-cis: IEEE Computational Intelligence Society
INNS: International Neural Network Society

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IEEE Press

Publication History

Published: 14 June 2009

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Cited By

Ma ZDai Q(2016)Selected an Stacking ELMs for Time Series PredictionNeural Processing Letters10.1007/s11063-016-9499-944:3(831-856)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1007/s11063-016-9499-9
Ma ZDai QLiu N(2015)Several novel evaluation measures for rank-based ensemble pruning with applications to time series predictionExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.07.04942:1(280-292)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1016/j.eswa.2014.07.049

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