NDLI: Improving financial time series prediction using exogenous series and neural networks committees

Content Provider	IEEE Xplore Digital Library
Author	Neto, M.C.A. Tavares, G. Alves, V.M.O. Cavalcanti, G.D.C. Tsang Ing Ren
Copyright Year	2010
Description	Author affiliation: Center of Informatics, Federal University of Pernambuco, Brazil (Cavalcanti, G.D.C.; Tsang Ing Ren) \|\| Facilit Technology Company, Brazil (Neto, M.C.A.; Tavares, G.; Alves, V.M.O.)
Abstract	Time series forecasting is useful in many researches areas. The use of models that provide a reliable prediction in financial time series may bring valuable profits for the investors. This paper proposes a methodology based on information obtained from exogenous series used in combination with neural networks to predict stock series. The best trained neural networks were used in combination to improve the prediction capacity of a single networks. To evaluate the proposed prediction models, some known metrics were applied. Moreover, we also proposed one new metric called Prediction in Direction and Accuracy (PDA), which benefits models with great performance in prediction accuracy and trend. Addictionally, there was used an evolutionary algorithm to choose the best trained models that maximize PDA. Experiments with two of the most important Brazilian companies stock quotes have shown the usefulness of the proposed prediction system to generate profits in investments.
Starting Page	1
Ending Page	8
File Size	730290
Page Count	8
File Format	PDF
ISBN	9781424469161
ISSN	10987576
e-ISBN	9781424469185
DOI	10.1109/IJCNN.2010.5596911
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-18
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis Measurement Mathematical model Predictive models Databases Artificial neural networks Biological system modeling
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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