dc.contributor.authorLiu, Yishan.
dc.date.accessioned2012-05-25T01:31:12Z
dc.date.available2012-05-25T01:31:12Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/10356/49837
dc.description.abstractStock Prediction is important for making sound investment decisions. Various machine learning approaches have been suggested for stock forecasting. However, due to the complexity and randomness of stock market, a precise prediction method remain unsolved now and highly demanded. In the Final Year Project (FYP), a new learning algorithm called Extreme Learning Machine (ELM) was utilized in the Financial Prediction System. Various technical indicators were employed to further study the trends and assist the prediction. From input selections, trading signaling, ELM filter, any stock can be selected as target; and the outputs will be next-days trend, buy or sell signal, trading profit results and recommendation.The experimental results show the training and prediction accuracy of the model are generally above 60% respectively, which concludes that leaning abilities of ELM (the acceptable prediction accuracy) and ELM based Financial Prediction System are excellent and which can meet the requirements of financial profit generationen_US
dc.format.extent52 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systemsen_US
dc.titleExtreme learning machine based financial predictionen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorHuang Guangbinen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US


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