Hybrid deep learning techniques integrated with machine learning for Foreign Exchange Rate Forecasting
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| Natura: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866901423881453568 |
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| author | Yu, Cui |
| author_facet | Yu, Cui |
| contents | <p>The foreign exchange is a significant financial market that attracts investors and countries seeking profitable investments. Despite the numerous techniques available for exchange rate forecasting, trend analysis, and accurate prediction, there is still a need for an automated and intelligent model to understand patterns and predict future trends. This research presents a novel hybrid deep learning model that forecasts the following year’s official exchange rate (LCU per USD) for global currencies based on historical annual exchange rates. The model performs technical feature analysis and predicts the upcoming year’s exchange rate trend for the USD currency. The Bi-LSTM architecture captures temporal values and processes them with random forest regression.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16794261 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Hybrid deep learning techniques integrated with machine learning for Foreign Exchange Rate Forecasting Yu, Cui <p>The foreign exchange is a significant financial market that attracts investors and countries seeking profitable investments. Despite the numerous techniques available for exchange rate forecasting, trend analysis, and accurate prediction, there is still a need for an automated and intelligent model to understand patterns and predict future trends. This research presents a novel hybrid deep learning model that forecasts the following year’s official exchange rate (LCU per USD) for global currencies based on historical annual exchange rates. The model performs technical feature analysis and predicts the upcoming year’s exchange rate trend for the USD currency. The Bi-LSTM architecture captures temporal values and processes them with random forest regression.</p> |
| title | Hybrid deep learning techniques integrated with machine learning for Foreign Exchange Rate Forecasting |
| url | https://doi.org/10.5281/zenodo.16794261 |