Increasing interpretability using a fuzzy-embedded recurrent neural network (FE-RNN) with its application in stock ETF trading
Deep learning has been a recent breakthrough that has enabled predictions and modelling to be very accurate. These predictions and modelling tools were once used to help us understand our data and serve as a tool to make a judgement. However, the vast improvements in these deep learning structures...
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| 格式: | Final Year Project |
| 語言: | English |
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Nanyang Technological University
2021
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| 在線閱讀: | https://hdl.handle.net/10356/148790 |
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| 機構: | Nanyang Technological University |
| 語言: | English |