Deep neural network approach integrated with reinforcement learning for forecasting exchange rates using time series data and influential factors

Deep neural network approach integrated with reinforcement learning for forecasting exchange rates using time series data and influential factors

Abstract Exchange rate forecasting is crucial for informed decision-making in financial markets, but significant challenges arise due to the high volatility and non-linear nature of economic time series. Traditional statistical models (ARIMA), state-of-the-art deep learning methods (LSTM, GRU), and...

Full description

Saved in:
Bibliographic Details
Main Authors: T. Soni Madhulatha, Dr. Md. Atheeq Sultan Ghori
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Exchange rate prediction
LSTM-DQN hybrid model
Time series forecasting
Reinforcement learning
USD/INR forecasting
Online Access:https://doi.org/10.1038/s41598-025-12516-3
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1038/s41598-025-12516-3

Similar Items