Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/52058
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dc.contributor.authorMalik, Arpit.
dc.date.accessioned2013-04-22T03:22:03Z
dc.date.available2013-04-22T03:22:03Z
dc.date.copyright2013en_US
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/10356/52058
dc.description.abstractFuzzy Associative Cortical Maps Architecture (FASCOM) is inspired from the cortical maps found in many biological and artificial neural systems. The cortical maps organise and represent information obtained from sensory inputs and play important roles in learning and memory processes. FASCOM uses features inspired by the structure and functions of cortical maps and is integrated a linguistic fuzzy model to perform associative learning of input-output pairs. The project undertakes to improve the architecture of FASCOM to incorporate a learning mechanism, so that the network is capable of modifying its properties on the basis of the incoming data leading to better prediction and higher accuracy. The author aims to validate the modified architecture of FASCOM by conducting benchmarking experiments and observing the improvement in the performance of the system over other systems. For this purpose, various classical datasets for classification and regression problems were used. The author worked on many real-life application to observe FASCOM++’s performance on real-life data. One of the applications is stock data prediction where the author used Hong Kong stock data and predicted prices using FASCOM++ and compared the results with the actual prices. The analysis of FASCOM++’s performance helps in gauging its practical use in real-life applications such as stock trading. The author simulated a simple stock trading algorithm to compare and evaluate FASCOM++’s performance against other architectures. The author explored other areas of applications and worked on options volatility prediction which is one of the core areas of research in the financial industry. By exploiting on the online learning capabilities FASCOM++ was able to perform better than the other architectures and demonstrated its capability to be a potential architecture for real-life purpose.en_US
dc.format.extent132 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleStock prediction, trading simulation and options volatility prediction using FASCOM++ (fuzzy associative cortical maps architecture)en_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorQuek Hiok Chaien_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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