Deep neural networks for financial time series forecasting
In today’s technologically advanced world, we see computers greatly replace many tasks due to their low cost, convenience, fast speed and high accuracy. Yet, with all the powerful technologies and instruments, it still remains challenging to predict future data using historical records. This is espe...
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| Formato: | Final Year Project (FYP) |
| Lenguaje: | English |
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Nanyang Technological University
2020
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| Acceso en línea: | https://hdl.handle.net/10356/140523 |
| _version_ | 1826120571032698880 |
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| author | Liu, Ting-Jun |
| author2 | Ponnuthurai Nagaratnam Suganthan |
| author_facet | Ponnuthurai Nagaratnam Suganthan Liu, Ting-Jun |
| author_sort | Liu, Ting-Jun |
| collection | NTU |
| description | In today’s technologically advanced world, we see computers greatly replace many tasks due to their low cost, convenience, fast speed and high accuracy. Yet, with all the powerful technologies and instruments, it still remains challenging to predict future data using historical records. This is especially so for investment purpose.
Machine learning and deep learning knowledge are widely used in predicting financial data. Many models have been established and the accuracies of models have been improved drastically over the years. In this paper, we would discuss and compare our proposed forecasting model with the developed ones. We mainly focus on the ensemble Random Vector Functional Link as a forecasting model together with Support Vector Regression and decomposition filters. This project aims to develop a more accurate forecasting model for financial time series. |
| first_indexed | 2024-10-01T05:18:38Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/140523 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:18:38Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1405232023-07-07T18:46:34Z Deep neural networks for financial time series forecasting Liu, Ting-Jun Ponnuthurai Nagaratnam Suganthan School of Electrical and Electronic Engineering epnsugan@ntu.edu.sg Engineering::Electrical and electronic engineering In today’s technologically advanced world, we see computers greatly replace many tasks due to their low cost, convenience, fast speed and high accuracy. Yet, with all the powerful technologies and instruments, it still remains challenging to predict future data using historical records. This is especially so for investment purpose. Machine learning and deep learning knowledge are widely used in predicting financial data. Many models have been established and the accuracies of models have been improved drastically over the years. In this paper, we would discuss and compare our proposed forecasting model with the developed ones. We mainly focus on the ensemble Random Vector Functional Link as a forecasting model together with Support Vector Regression and decomposition filters. This project aims to develop a more accurate forecasting model for financial time series. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-29T12:21:28Z 2020-05-29T12:21:28Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/140523 en A1125-191 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Liu, Ting-Jun Deep neural networks for financial time series forecasting |
| title | Deep neural networks for financial time series forecasting |
| title_full | Deep neural networks for financial time series forecasting |
| title_fullStr | Deep neural networks for financial time series forecasting |
| title_full_unstemmed | Deep neural networks for financial time series forecasting |
| title_short | Deep neural networks for financial time series forecasting |
| title_sort | deep neural networks for financial time series forecasting |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/140523 |
| work_keys_str_mv | AT liutingjun deepneuralnetworksforfinancialtimeseriesforecasting |