فهرست نشریات علمی دسترسی آزاد ایران

Stock price forecasting poses significant challenges due to non-stationarity, nonlinearity, and noise in financial markets, particularly for the Iraqi stock exchange. This study proposes an enhanced interval-valued forecasting model for daily prices of the Al Mansour Pharmaceutical Industries (MPI) company (2020–2025) using v-support vector regression (VSVR) with hyperparameters optimized via the waterwheel plant algorithm (WWPOA). The WWPOA approach tunes key VSVR parameters through population-based exploration and exploitation phases inspired by WWPOA, outperforming grid search (GS-VSVR) and cross-validation (CV-VSVR). Forecasting performance is evaluated using four criteria, namely mean absolute error , root mean squared error , direction accuracy and coefficient of determination . The empirical results show that the proposed model achieves lower values, compared to and indicating superior accuracy, robustness, and directional forecasting capability. On training data (637 days), WWPOA-VSVR achieves superior metrics for center and radius compared to baselines; testing results (308 days) confirm robustness. Further, Diebold-Mariano tests validate center-based WWPOA-VSVR superiority over radius-based at 95% confidence (p<0.05). On the training set, the center based WWPOA VSVR achieves MAE of about 0.18, RMSE of about 0.28, DA around 0.63, and R 2 close to 0.93, while the radius based model attains MAE near 0.19, RMSE around 0.29, DA about 0.61, and R 2 near 0.92. On the test set, center forecasts retain strong performance, with MAE around 0.20, RMSE about 0.30, DA near 0.60, and R 2 approximately 0.92, and radius forecasts achieve MAE close to 0.17, RMSE near 0.27, DA about 0.57, and R 2 around 0.88.