预测编号 | 27 | 28 | 29 | 30 | 31 | 32 | 均误差 | |
实际值/t | 6,214,550.78 | 3,870,301.52 | 3,996,678.71 | 3,870,329.96 | 3,997,936.11 | 1,323,495.35 | ||
NSGAII -GPR | 预测值/t | 6,251,313.89 | 4,064,342.571 | 3,997,010.766 | 3,879,803.683 | 3,997,979.927 | 1,464,708.447 | 0.063 |
绝对误差 | 36,763.11547 | 194,041.0527 | 332.0560011 | 9473.71931 | 43.81964796 | 141,213.1009 | ||
相对误差 | 0.005915651 | 0.050135901 | 0.00000831 | 0.002447781 | 0.000011 | 0.106697089 | ||
GM(1,1) | 预测值/t | 4,423,160.01 | 4,729,416.46 | 4,748,834.65 | 4,797,843.14 | 4,817,569.12 | 5,163,109.27 | 0.604 |
绝对误差 | 1,791,406.34 | 859,114.95 | 752,155.94 | 927,513.18 | 819,633.01 | 3,839,613.92 | ||
相对误差 | 0.26 | 0.20 | 0.17 | 0.22 | 0.18 | 2.60 | ||
SVM | 预测值/t | 6,482,462.66 | 3,874,287.05 | 4,029,607.41 | 3,790,629.42 | 4,036,552.51 | 4,232,326.82 | 0.380 |
绝对误差 | 267,911.89 | 3985.53 | 32,928.70 | 79,700.55 | 38,616.40 | 2,908,831.48 | ||
相对误差 | 0.043 | 0.0010 | 0.0082 | 0.021 | 0.0098 | 2.20 | ||
人工神经网络 | 预测值/t | 5,314,739.82 | 3,777,524.64 | 4,123,721.17 | 3,509,440.10 | 4,162,545.59 | 2,025,618.80 | 0.144 |
绝对误差 | 899,810.95 | 92,776.88 | 127,042.46 | 360,889.86 | 164,609.48 | 702,123.45 | ||
相对误差 | 0.15 | 0.024 | 0.032 | 0.093 | 0.041 | 0.53 |