Enhanced One-Day-Ahead AUD/USD Exchange Rate Prediction using CatBoost Model

Autor

  • Syed Ahmad Bin Syed Alwee, Motorola Solutions (M) Sdn Bhd, Bayan Lepas, Penang, Malaysia
  • Farah Shahnaz Feroz Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, Malaysia
  • Minli Chai Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, Malaysia
  • Farhannah Shahfinaz Binti Feroz Bahagian Pengurusan Sumber Manusia, Jabatan Ketua Menteri Melaka, Melaka, Malaysia
  • Mohd Fauzi Ab Rahman Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, Malaysia

DOI:

https://doi.org/10.24425/ijet.2026.157905

Keywords

AUD/USD forecasting, Forex prediction, exchange rate prediction, CatBoost,, machine learning, algorithmic trading, technical indicators, explainable AI (XAI), financial forecasting, time series prediction

Abstrakt

The foreign exchange (Forex) market is highly liquid
and volatile, making accurate short-term forecasting both critical
and challenging. This study investigates one-day-ahead AUD/USD
exchange rate prediction using CatBoost, Random Forest (RF),
and Support Vector Machine (SVM) machine learning (ML)
models with continuous and discretized technical indicators. Ten
technical indicators were derived from 5,027 historical data points.
This is the first study to apply discrete technical indicators with
CatBoost for recent AUD/USD price forecasting. Results showed
that CatBoost achieved the highest accuracy (89.68%) and AUC
(0.9609) on the discretised dataset. Statistical test confirmed the
significance of CatBoost’s superior performance, highlighting its
potential to enhance predictive performance and support real-time
decision-making in Forex trading.

Pobrania

Opublikowane

2026-06-02

Jak cytować

Alwee, Syed Ahmad Bin Syed, i in. „Enhanced One-Day-Ahead AUD USD Exchange Rate Prediction Using CatBoost Model”. International Journal of Electronics and Telecommunications, t. 72, nr 2, czerwiec 2026, s. 1-7, doi:10.24425/ijet.2026.157905.

Numer

Tom 72 Nr 2 (2026)

Dział

Artykuły

Licencja

Prawa autorskie (c) 2026 International Journal of Electronics and Telecommunications

Creative Commons License

Utwór dostępny jest na licencji Creative Commons Uznanie autorstwa 4.0 Międzynarodowe.


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