Critical comparisons on deep learning approaches for foreign exchange rate prediction

TL;DR

This paper compares deep learning models like RNN, LSTM, and BP neural networks for foreign exchange rate prediction, focusing on their real-time update capabilities and prediction accuracy.

Contribution

It provides a detailed analysis of the fundamental theories and characteristics of RNN, LSTM, and BP neural networks for forex prediction.

Findings

Analyzes the advantages and disadvantages of RNN, LSTM, and BP neural networks.

Discusses the suitability of each model for real-time forex prediction.

Provides guidance for selecting optimal prediction models based on characteristics.

Abstract

In a natural market environment, the price prediction model needs to be updated in real time according to the data obtained by the system to ensure the accuracy of the prediction. In order to improve the user experience of the system, the price prediction function needs to use the fastest training model and the model prediction fitting effect of the best network as a predictive model. We conduct research on the fundamental theories of RNN, LSTM, and BP neural networks, analyse their respective characteristics, and discuss their advantages and disadvantages to provide a reference for the selection of price-prediction models.