AI-driven stock market prediction
The accuracy of deep learning techniques used for prediction has always been deemed superior as compared to regression techniques. In this report, deep learning techniques such as Long Short-Term Memory, Recurrent Neural Network, Multi-Layer Perceptron and Gated Recurrent Unit will be used in...
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| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2022
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/157648 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | The accuracy of deep learning techniques used for prediction has always been deemed superior
as compared to regression techniques. In this report, deep learning techniques such as Long
Short-Term Memory, Recurrent Neural Network, Multi-Layer Perceptron and Gated Recurrent
Unit will be used in a comparison with regression techniques such as Gradient Boosting
Regressor and Support Vector Regressor to forecast the Straits Times Index (STI). The data
sourced will also be non-linear and will be used as inputs into the algorithms to generate the
results. The results will be compared using Fundamental Analysis and Technical Analysis. This
experiment shows that the results from deep learning techniques does not generally mean that
it is more accurate as compared to regression techniques. |
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