An Investigation on Forecasting of Indian Rupee Performance Against Global Currencies Using Hybrid Deep Learning Models

DOI

10.2991/978-94-6463-872-1_4How to use a DOI?

Keywords

Deep learning; Neural network; Convolutional neural network; Time series analysis; Exchange rate prediction; LSTM; CNN; FOREX Forecasting; Currency Risk Management; Strategic Financial Planning; Managerial Decision Support

Abstract

Hybrid models have gained popularity due to their capacity to identify intricate patterns in time-series data using deep learning architectures. Accurate exchange rate forecasting is essential for risk management and economic decision-making in the extremely unpredictable Forex market. to predict how the Indian Rupee will fluctuate in value with relation to the US dollar, the British pound, and the euro. This study assesses the performance of three hybrid deep learning models: Convolutional Recurrent Neural Network (CRNN), Convolutional Neural Network with Long Short-Term Memory (CNN + LSTM), and Autoencoder with LSTM. Our findings show that the CNN + LSTM model consistently performs better than the other models, producing predictions that are more reliable and accurate. The Autoencoder + LSTM model performs poorly, despite CRNN’s respectable performance, suggesting that more tuning is required. This study emphasizes how hybrid models may improve Forex forecasts and how important it is to choose the right hybrid models for particular financial jobs. These projections give investors, financial managers, and legislators critical information for organizing, hedging, and reducing currency risk in international activities.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Neeraj Sharma
AU  - Vaibhav Jain
PY  - 2025
DA  - 2025/11/04
TI  - An Investigation on Forecasting of Indian Rupee Performance Against Global Currencies Using Hybrid Deep Learning Models
BT  - Proceedings of the 2nd International Conference on Sustainable Business Practices and Innovative Models (ICSBPIM-2025)
PB  - Atlantis Press
SP  - 35
EP  - 42
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-872-1_4
DO  - 10.2991/978-94-6463-872-1_4
ID  - Sharma2025
ER  -