A comparative study of multivariate CNN, BiLSTM and hybrid CNN–BiLSTM models for forecasting foreign exchange rate using deep learning

A comparative study of multivariate CNN, BiLSTM and hybrid CNN–BiLSTM models for forecasting foreign exchange rate using deep learning

This study evaluates the forecasting capabilities of multivariate Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and a hybrid CNN-BiLSTM model for predicting daily rate returns of USD, EUR and GBP in Rwanda’s foreign exchange market from 2012 to 2025. While CNN ef...

Full description

Saved in:
Bibliographic Details
Main Authors: Elysee Nsengiyumva, Joseph K. Mung’atu, Charles Ruranga
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Cogent Economics & Finance
Subjects:
Multivariate
CNN-BiLSTM model
foreign exchange
forecasting
deep learning
Economics
Online Access:https://www.tandfonline.com/doi/10.1080/23322039.2025.2526148
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.tandfonline.com/doi/10.1080/23322039.2025.2526148

Similar Items