From: A comprehensive evaluation of ensemble learning for stock-market prediction
Models | No. of estimators | RMSE | MAE | R2 | EVS | MedAE | RMSLE | Train time | Test time |
---|---|---|---|---|---|---|---|---|---|
DTBagr | 1 | 0.266 | 0.213 | 0.449 | 0.451 | 0.210 | 0.129 | 0.014 | 0.004 |
SVMBagr | 1 | 0.110 | 0.069 | 0.906 | 0.908 | 0.051 | 0.051 | 0.007 | 0.001 |
MLPBagr | 1 | 0.061 | 0.034 | 0.971 | 0.972 | 0.021 | 0.028 | 0.141 | 0.002 |
DTBotr | 1 | 0.104 | 0.073 | 0.916 | 0.917 | 0.052 | 0.046 | 0.004 | 0.000 |
SVMBotr | 1 | 0.081 | 0.057 | 0.949 | 0.949 | 0.048 | 0.040 | 0.005 | 0.001 |
MLPBotr | 1 | 0.036 | 0.026 | 0.990 | 0.991 | 0.023 | 0.019 | 0.187 | 0.001 |
DTBagr | 5 | 0.091 | 0.069 | 0.935 | 0.935 | 0.056 | 0.041 | 0.015 | 0.002 |
SVMBagr | 5 | 0.081 | 0.057 | 0.949 | 0.949 | 0.046 | 0.039 | 0.013 | 0.003 |
MLPBagr | 5 | 0.026 | 0.014 | 0.995 | 0.995 | 0.008 | 0.013 | 0.678 | 0.003 |
DTBotr | 5 | 0.080 | 0.060 | 0.950 | 0.951 | 0.047 | 0.036 | 0.014 | 0.001 |
SVMBotr | 5 | 0.060 | 0.049 | 0.972 | 0.973 | 0.046 | 0.030 | 0.021 | 0.004 |
MLPBotr | 5 | 0.027 | 0.017 | 0.994 | 0.995 | 0.013 | 0.013 | 0.799 | 0.003 |
DTBagr | 15 | 0.083 | 0.069 | 0.946 | 0.946 | 0.063 | 0.039 | 0.038 | 0.004 |
SVMBagr | 15 | 0.076 | 0.056 | 0.956 | 0.956 | 0.047 | 0.037 | 0.034 | 0.011 |
MLPBagr | 15 | 0.021 | 0.014 | 0.996 | 0.997 | 0.010 | 0.010 | 3.829 | 0.006 |
DTBotr | 15 | 0.069 | 0.054 | 0.963 | 0.963 | 0.044 | 0.032 | 0.096 | 0.012 |
SVMBotr | 15 | 0.057 | 0.047 | 0.975 | 0.975 | 0.043 | 0.029 | 0.224 | 0.039 |
MLPBotr | 15 | 0.016 | 0.011 | 0.998 | 0.998 | 0.008 | 0.007 | 4.987 | 0.023 |
DTBagr | 20 | 0.089 | 0.075 | 0.939 | 0.939 | 0.072 | 0.042 | 0.068 | 0.006 |
SVMBagr | 20 | 0.075 | 0.055 | 0.956 | 0.956 | 0.044 | 0.037 | 0.040 | 0.013 |
MLPBagr | 20 | 0.021 | 0.014 | 0.996 | 0.997 | 0.010 | 0.010 | 4.038 | 0.021 |
DTBotr | 20 | 0.077 | 0.057 | 0.954 | 0.955 | 0.044 | 0.033 | 0.058 | 0.006 |
SVMBotr | 20 | 0.057 | 0.047 | 0.975 | 0.976 | 0.043 | 0.028 | 0.089 | 0.020 |
MLPBotr | 20 | 0.014 | 0.010 | 0.999 | 0.999 | 0.008 | 0.006 | 4.824 | 0.009 |
DTBagr | 50 | 0.078 | 0.064 | 0.953 | 0.953 | 0.057 | 0.037 | 0.114 | 0.010 |
SVMBagr | 50 | 0.072 | 0.054 | 0.960 | 0.960 | 0.044 | 0.036 | 0.226 | 0.056 |
MLPBagr | 50 | 0.014 | 0.010 | 0.999 | 0.999 | 0.007 | 0.007 | 13.967 | 0.021 |
DTBotr | 50 | 0.047 | 0.038 | 0.983 | 0.983 | 0.033 | 0.023 | 0.211 | 0.010 |
SVMBotr | 50 | 0.056 | 0.047 | 0.975 | 0.976 | 0.044 | 0.028 | 0.186 | 0.039 |
MLPBotr | 50 | 0.013 | 0.009 | 0.999 | 0.999 | 0.006 | 0.006 | 22.515 | 0.019 |
DTBagr | 100 | 0.075 | 0.061 | 0.956 | 0.956 | 0.055 | 0.036 | 0.325 | 0.045 |
SVMBagr | 100 | 0.073 | 0.054 | 0.959 | 0.959 | 0.045 | 0.036 | 0.559 | 0.127 |
MLPBagr | 100 | 0.014 | 0.010 | 0.999 | 0.999 | 0.008 | 0.007 | 34.135 | 0.053 |
DTBotr | 100 | 0.039 | 0.032 | 0.988 | 0.988 | 0.029 | 0.020 | 0.315 | 0.021 |
SVMBotr | 100 | 0.056 | 0.047 | 0.975 | 0.976 | 0.044 | 0.028 | 0.787 | 0.357 |
MLPBotr | 100 | 0.011 | 0.008 | 0.999 | 0.999 | 0.006 | 0.005 | 28.182 | 0.039 |
DTBagr | 150 | 0.073 | 0.059 | 0.959 | 0.959 | 0.053 | 0.035 | 0.339 | 0.042 |
SVMBagr | 150 | 0.072 | 0.054 | 0.960 | 0.960 | 0.045 | 0.036 | 0.401 | 0.119 |
MLPBagr | 150 | 0.013 | 0.010 | 0.999 | 0.999 | 0.008 | 0.007 | 30.237 | 0.082 |
DTBotr | 150 | 0.038 | 0.031 | 0.989 | 0.990 | 0.028 | 0.019 | 0.345 | 0.024 |
SVMBotr | 150 | 0.056 | 0.047 | 0.975 | 0.976 | 0.044 | 0.028 | 0.115 | 0.019 |
MLPBotr | 150 | 0.011 | 0.008 | 0.999 | 0.999 | 0.006 | 0.005 | 24.404 | 0.187 |
DTBagr | 200 | 0.073 | 0.060 | 0.958 | 0.958 | 0.055 | 0.035 | 0.786 | 0.043 |
SVMBagr | 200 | 0.071 | 0.054 | 0.960 | 0.961 | 0.044 | 0.035 | 0.487 | 0.100 |
MLPBagr | 200 | 0.013 | 0.010 | 0.999 | 0.999 | 0.008 | 0.007 | 38.432 | 0.091 |
DTBotr | 200 | 0.035 | 0.028 | 0.990 | 0.991 | 0.024 | 0.018 | 0.453 | 0.027 |
SVMBotr | 200 | 0.056 | 0.047 | 0.975 | 0.976 | 0.044 | 0.028 | 0.126 | 0.021 |
MLPBotr | 200 | 0.011 | 0.008 | 0.999 | 0.999 | 0.006 | 0.005 | 24.178 | 0.039 |