Applications of Some Deep Learning Algorithms to Predict Trend in the Forex Exchange Market

Key Points

The accuracy of deep learning algorithms improves trend prediction in the Forex market, showing more potential than traditional methods.
Temporal fusion transformer and multilayer perceptrons show significant capability in analyzing EUR/USD data for trading.
Analysis utilizes advanced deep learning techniques like extreme learning machine classification for time series forecasting.
This analysis indicates the need for modern tools in financial markets, emphasizing the limitations of classical statistical approaches.

Abstract

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.

Applications of Some Deep Learning Algorithms to Predict Trend in the Forex Exchange Market

Key Points

Abstract

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