Table 5.
Performance comparison of different techniques.
| MAPE | RMSE | MAE | IR(MAPE) | ||
|---|---|---|---|---|---|
| BPNN | 1 | 46.78% | 11.1041 | 8.4084 | – |
| 2 | 9.56% | 2.2285 | 1.6749 | 79.56% | |
| 3 | 28.51% | 6.6743 | 5.3789 | 39.06% | |
| 4 | 7.17% | 1.9194 | 1.5775 | 84.67% | |
| MLR | 1 | 76.27% | 14.5121 | 12.8406 | – |
| 2 | 76.19% | 14.6423 | 12.7107 | 0.10% | |
| 3 | 64.45% | 15.4351 | 12.0035 | 15.50% | |
| 4 | 56.28% | 12.9765 | 10.4790 | 26.21% | |
| SVM | 1 | 36.17% | 8.9761 | 7.4284 | – |
| 2 | 26.97% | 7.1991 | 6.4495 | 19.20% | |
| 3 | 46.59% | 14.4929 | 10.1242 | −28.81% | |
| 4 | 23.60% | 5.2307 | 4.0115 | 34.75% | |
| LSTM | 1 | 54.43% | 14.5938 | 11.1827 | – |
| 2 | 32.53% | 9.7595 | 8.1046 | 40.24% | |
| 3 | 44.40% | 8.2702 | 6.9034 | 18.43% | |
| 4 | 31.88% | 6.7175 | 5.2260 | 41.43% | |
| RNN | 1 | 58.92% | 12.3870 | 11.2355 | – |
| 2 | 22.53% | 4.7991 | 4.4857 | 61.76% | |
| 3 | 50.54% | 15.0736 | 11.4218 | 14.22% | |
| 4 | 17.02% | 4.7706 | 3.3586 | 71.11% |
Note: “1” means that historical data are used to predict. “2” means that historical data and text features are used to predict. “3” means that historical data and financial data are used to predict. “4” means that historical data, financial data, and text features data are both employed to predict. IR(MAPE) means improving the rate of MAPE from “1” to “2 (3 or 4)”. The grid search method is used to determine the parameters of adopted algorithms [37]. Appendix C lists the final parameter values of these forecasting models in all examples.