Forecasting AUD/USD forex trends using advanced CNN-Based hybrid models
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Authors
Varastehpour, S.
Abdolahi, A.
Modares, A.F.A.
Varastehpour, Soheil
Abdolahi, A.
Modares, A.F.A.
Varastehpour, Soheil
Author ORCID Profiles (clickable)
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Grantor
Date
2025-07
Supervisors
Type
Conference Contribution - Paper in Published Proceedings
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
forex
foreign exchange markets
prediction
Long Short-Term Memory (LSTM)
Convolutional Neural Network (CNN)
Bidirectional Long Short-Term Memory (BiLSTM)
deep-learning algorithms
computer modelling
algorithms
foreign exchange markets
prediction
Long Short-Term Memory (LSTM)
Convolutional Neural Network (CNN)
Bidirectional Long Short-Term Memory (BiLSTM)
deep-learning algorithms
computer modelling
algorithms
ANZSRC Field of Research Code (2020)
Citation
Varastehpour, S., Abdolahi, A., Modares, A. F. A., & Varastehpour, S. (2025). Forecasting AUD/USD Forex Trends Using Advanced CNN-Based Hybrid Models. In S. Varastehpour & M. Shakiba (Eds.), Proceedings: AIOT Global Summit 2025: Economic Growth, 15–16 July (pp. 87–92). ePress, Unitec. https://doi.org/10.34074/proc.250116
Abstract
Trading foreign currencies worth trillions of dollars takes place daily in the Forex market, characterised by highly volatile movements. One approach to mitigating risk in Forex trading decisions is through forecasting techniques. In this research study, we investigate the effectiveness of advanced deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), transformer and their hybrid combinations for forecasting exchange rates.
Publisher
Unitec ePress
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Link to ePress publication
DOI
https://doi.org/10.34074/proc.250116
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CC BY-NC Attribution-NonCommercial 4.0 International