Forecasting Currency Exchange Direction with an Advanced Immune-Inspired Model

Accurately forecasting currency exchange rates is a persistent and significant challenge in computational finance. This study addresses the challenge by introducing an advanced model based on the Artificial Immune Recognition System (AIRS), an algorithm inspired by the adaptive learning of biological immune systems, to predict the directional movement of the EUR/USD pair. While conventional machine learning models are widely used, immune-inspired approaches have been largely unexplored in this domain. Using historical data from May 2002 to July 2024, the proposed model was rigorously optimized through time-series cross-validation and an Evolutionary Algorithm search. On the out-of-sample test set, the optimized model demonstrates strong predictive power, achieving an F1-Score of 0.66 and an ROC AUC of 0.74, results that are competitive with standard machine learning benchmarks. These findings validate AIRS as a robust and scientifically defensible tool for financial forecasting, offering a viable alternative to conventional methods in a highly volatile market.

@article{Mohamed2025,
  title     = {Forecasting Currency Exchange Direction with an Advanced Immune-Inspired Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {EL BADAOUI Mohamed and RAOUYANE Brahim and EL MOUMEN Samira and BELLAFKIH Mostafa},
  doi       = {10.14569/IJACSA.2025.0160828},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160828}
}