Deep GRU neural networks for Apple stock price prediction
The stock market's ever-evolving landscape is characterized by its capricious nature, rendering the task of stock price prediction highly intricate. The intertwining variables such as global political scenarios, company performance, and public sentiment contribute to the volatility of stock pri...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/172724 |
| _version_ | 1826110127639363584 |
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| author | Ji, YiJun |
| author2 | Wang Lipo |
| author_facet | Wang Lipo Ji, YiJun |
| author_sort | Ji, YiJun |
| collection | NTU |
| description | The stock market's ever-evolving landscape is characterized by its capricious nature, rendering the task of stock price prediction highly intricate. The intertwining variables such as global political scenarios, company performance, and public sentiment contribute to the volatility of stock prices, making predictions even more sophisticated. However, the rapidly advancing realm of machine learning and deep learning like Gated Recurrent Unit (GRU) has begun to hold significant promise in tackling these challenges.
This project aims to use GRU network to predict gold price using its historic value and evaluating its accuracy with other traditional neural networks. |
| first_indexed | 2024-10-01T02:29:24Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/172724 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:29:24Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1727242023-12-22T15:41:58Z Deep GRU neural networks for Apple stock price prediction Ji, YiJun Wang Lipo School of Electrical and Electronic Engineering ELPWang@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems The stock market's ever-evolving landscape is characterized by its capricious nature, rendering the task of stock price prediction highly intricate. The intertwining variables such as global political scenarios, company performance, and public sentiment contribute to the volatility of stock prices, making predictions even more sophisticated. However, the rapidly advancing realm of machine learning and deep learning like Gated Recurrent Unit (GRU) has begun to hold significant promise in tackling these challenges. This project aims to use GRU network to predict gold price using its historic value and evaluating its accuracy with other traditional neural networks. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-12-19T05:14:21Z 2023-12-19T05:14:21Z 2023 Final Year Project (FYP) Ji, Y. (2023). Deep GRU neural networks for Apple stock price prediction. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/172724 https://hdl.handle.net/10356/172724 en A3313-222 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Computer hardware, software and systems Ji, YiJun Deep GRU neural networks for Apple stock price prediction |
| title | Deep GRU neural networks for Apple stock price prediction |
| title_full | Deep GRU neural networks for Apple stock price prediction |
| title_fullStr | Deep GRU neural networks for Apple stock price prediction |
| title_full_unstemmed | Deep GRU neural networks for Apple stock price prediction |
| title_short | Deep GRU neural networks for Apple stock price prediction |
| title_sort | deep gru neural networks for apple stock price prediction |
| topic | Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | https://hdl.handle.net/10356/172724 |
| work_keys_str_mv | AT jiyijun deepgruneuralnetworksforapplestockpriceprediction |