RBM neural networks for Apple stock price
Forecasting stocks has always been demanding as the stock market is volatile and ever changing with unforeseen variables in play. This report will use Artificial Neural Networks (ANN), specifically the Restricted Boltzmann Machine (RBM), to predict trends and patterns unseen to the naked eye. Th...
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Nanyang Technological University
2023
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sg-ntu-dr.10356-1682832023-07-07T15:43:10Z RBM neural networks for Apple stock price Chew, Daniel Zi Ping Wang Lipo School of Electrical and Electronic Engineering ELPWang@ntu.edu.sg Engineering::Electrical and electronic engineering Forecasting stocks has always been demanding as the stock market is volatile and ever changing with unforeseen variables in play. This report will use Artificial Neural Networks (ANN), specifically the Restricted Boltzmann Machine (RBM), to predict trends and patterns unseen to the naked eye. The usage of RBM will aid in improving the accuracy of the forecast. Experiments are conducted to evaluate the usefulness of RBM in the forecasting of Apple stocks. The results suggest that RBM should not be solely relied on, but along with other analytical tools for greater reliance. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-06-09T12:41:33Z 2023-06-09T12:41:33Z 2023 Final Year Project (FYP) Chew, D. Z. P. (2023). RBM neural networks for Apple stock price. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/168283 https://hdl.handle.net/10356/168283 en A3285-211 application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Chew, Daniel Zi Ping RBM neural networks for Apple stock price |
| description |
Forecasting stocks has always been demanding as the stock market is volatile and ever changing with unforeseen variables in play. This report will use Artificial Neural Networks
(ANN), specifically the Restricted Boltzmann Machine (RBM), to predict trends and
patterns unseen to the naked eye. The usage of RBM will aid in improving the accuracy of
the forecast. Experiments are conducted to evaluate the usefulness of RBM in the
forecasting of Apple stocks. The results suggest that RBM should not be solely relied on,
but along with other analytical tools for greater reliance. |
| author2 |
Wang Lipo |
| author_facet |
Wang Lipo Chew, Daniel Zi Ping |
| format |
Final Year Project |
| author |
Chew, Daniel Zi Ping |
| author_sort |
Chew, Daniel Zi Ping |
| title |
RBM neural networks for Apple stock price |
| title_short |
RBM neural networks for Apple stock price |
| title_full |
RBM neural networks for Apple stock price |
| title_fullStr |
RBM neural networks for Apple stock price |
| title_full_unstemmed |
RBM neural networks for Apple stock price |
| title_sort |
rbm neural networks for apple stock price |
| publisher |
Nanyang Technological University |
| publishDate |
2023 |
| url |
https://hdl.handle.net/10356/168283 |
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1772825712670015488 |