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Applications of Some Deep Learning Algorithms to Predict Trend in the Forex Exchange Market | ||
| Journal of Mathematics and Modeling in Finance | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 26 مرداد 1404 اصل مقاله (423.38 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22054/jmmf.2025.85949.1183 | ||
| نویسندگان | ||
| Mohammad Ali Jafari* ؛ Sina Ghasemilo | ||
| Financial Mathematics Department, Finance Faculty, Kharazmi University | ||
| چکیده | ||
| Predicting time series has always been one of the challenges in the financial markets. With the increase in the amount of data, the need to use modern tools instead of classical statistical and time series methods has become clear. In this paper, some deep learning algorithms such as Multilayer Perceptrons (MLPs), Keras Classification, Temporal Fusion Transformer (TFT, developed by Google), Extreme Learning Machine Classification (ELMC) and Propagation Hierarical Learning Network (PHILNet) are used for trading on the foreign exchange market. The efficiency and accuracy of these algorithms are presented. In this order, the EUR/USD data is used as input for the above algorithms. | ||
| کلیدواژه‌ها | ||
| Deep learning؛ Forex market؛ Trend of the EUR/USD | ||
| مراجع | ||
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