Research Article
Comparing the Forecast Performance of Advanced Statistical and Machine Learning Techniques Using Huge Big Data: Evidence from Monte Carlo Experiments
Table 3
Forecast comparison under autocorrelation from Monte Carlo simulation (Scenario 3).
| | Models | ρ = 0.25, = 50 | ρ = 0.25, = 70 |
| | n = 100/200/400 | RMSE | MAE | RMSE | MAE | | MCP | 1.167/1.078/1.056 | 0.943/0.866/0.845 | 1.254/1.110/1.065 | 1.012/0.892/0.851 | | E-SCAD | 1.175/1.091/1.062 | 0.952/0.877/0.850 | 1.241/1.124/1.074 | 1.002/0.904/0.859 | | Autometrics | 1.192/1.100/1.064 | 0.963/0.884/0.851 | 1.392/1.126/1.071 | 1.121/0.908/0.858 | | FM_PCA | 3.520/3.222/2.858 | 2.848/2.589/2.288 | 4.569/4.274/3.952 | 3.695/3.429/3.165 | | FM_PLS | 1.568/1.231/1.119 | 1.268/0.990/0.896 | 1.972/1.367/1.166 | 1.591/1.101/0.932 |
| | n = 100/200/400 | ρ = 0.50, = 50 | ρ = 0.50, = 70 | | MCP | 1.324/1.222/1.185 | 1.073/0.987/0.949 | 1.448/1.234/1.197 | 1.177/0.993/0.957 | | E-SCAD | 1.318/1.238/1.191 | 1.068/0.996/0.954 | 1.382/1.248/1.206 | 1.122/1.005/0.965 | | Autometrics | 1.330/1.222/1.187 | 1.080/0.985/0.951 | 1.630/1.255/1.202 | 1.318/1.011/0.964 | | FM_PCA | 3.570/3.279/2.916 | 2.889/2.624/2.333 | 4.607/4.247/4.021 | 3.716/3.381/3.219 | | FM_PLS | 1.720/1.392/1.258 | 1.389/1.121/1.005 | 2.108/1.503/1.303 | 1.702/1.206/1.042 |
| | n = 100/200/400 | ρ = 0.90, = 50 | ρ = 0.90, = 70 | | MCP | 2.953/2.408/2.364 | 2.449/1.997/1.936 | 3.608/2.538/2.368 | 2.961/2.100/1.940 | | E-SCAD | 2.714/2.380/2.366 | 2.267/1.976/1.937 | 3.039/2.498/2.370 | 2.525/2.069/1.941 | | Autometrics | 3.250/2.480/2.358 | 2.693/2.049/1.930 | 4.273/2.594/2.394 | 3.494/2.146/1.957 | | FM_PCA | 4.165/3.871/3.563 | 3.387/3.126/2.868 | 5.051/4.735/4.506 | 4.111/3.810/3.609 | | FM_PLS | 2.941/2.579/2.476 | 2.439/2.122/2.020 | 3.341/2.796/2.544 | 2.749/2.293/2.072 |
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Note. Bold values indicate a better forecast.
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