Research Article
Financial Time Series Prediction Using Elman Recurrent Random Neural Networks
Table 4
Comparisons of indices’ predictions for different forecasting models.
| Index errors | BPNN | STNN | ERNN | ST-ERNN |
| SSE | MAE | 45.3701 | 24.9687 | 37.262647 | 12.7390 | RMSE | 54.4564 | 40.5437 | 49.3907 | 37.0693 | MAPE | 20.1994 | 11.8947 | 18.2110 | 4.1353 | MAPE(100) | 5.0644 | 3.6868 | 4.3176 | 2.6809 |
| TWSE | MAE | 252.7225 | 140.5971 | 151.2830 | 105.6377 | RMSE | 316.8197 | 186.8309 | 205.4236 | 136.1674 | MAPE | 3.2017 | 1.7303 | 1.8449 | 1.3468 | MAPE(100) | 2.2135 | 1.1494 | 1.3349 | 1.2601 |
| KOSPI | MAE | 74.3073 | 56.3309 | 47.9296 | 18.2421 | RMSE | 77.1528 | 58.2944 | 50.8174 | 21.0479 | MAPE | 16.6084 | 12.4461 | 10.9608 | 4.2257 | MAPE(100) | 7.4379 | 5.9664 | 4.9176 | 2.1788 |
| Nikkei225 | MAE | 203.8034 | 138.1857 | 166.2480 | 68.5458 | RMSE | 238.5933 | 169.7061 | 207.3395 | 89.0378 | MAPE | 1.8556 | 1.2580 | 1.5398 | 0.6010 | MAPE(100) | 0.7674 | 0.5191 | 0.4962 | 0.4261 |
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