Case Study: Predicting Future Forex Prices Using MLP and LSTM Models
| Author | Mesleh, Mahmoud |
| Author | Kiranyaz, Mustafa |
| Available date | 2024-07-24T10:13:52Z |
| Publication Date | 2021 |
| Publication Name | Proceedings - 2021 2nd International Conference on Electronics, Communications and Information Technology, CECIT 2021 |
| Resource | Scopus |
| Abstract | This study compiles 10 years of past daily closing prices for numerous trading assets, and economic reports. The data will be used to train, test, and validate a variety of ANN and LSTM models. This paper introduces a learning window that trains the network using a predefined number of days in the past to predict several days' closing prices in the future. A search grid algorithm is used to select the optimal hyperparameters for the networks. The MLP network managed to achieve an accuracy of above 80% when 30 days of the previous closing price were used as input to the network. |
| Language | en |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Subject | ANN artificial neural networks financial market Forex LSTM MLP prediction RNN |
| Type | Conference Paper |
| Pagination | 343-346 |
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Electrical Engineering [2554 items ]