Predicting stock high price using forecast error with recurrent neural network
Publié en ligne: 25 mai 2021
Pages: 283 - 292
Reçu: 24 déc. 2020
Accepté: 11 avr. 2021
© 2021 Zhiguo Bao et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
The experiment results (NN1: GRU, DATA SET: 000001.SH).
Models |
Time |
RMSE |
MAPE |
MASE |
Dstat |
GRU |
0.090 |
28.676 |
0.683 |
1.281 |
0.693 |
GRU,GRU |
0.178 |
21.269 |
0.449 |
0.837 |
0.713 |
GRU,LSTM |
0.193 |
21.319 |
0.450 |
0.840 |
0.703 |
GRU,MLP |
0.112 |
23.559 |
0.517 |
0.968 |
0.710 |
The standard deviation of experimental data.
Data set |
Training |
Test |
000001.SH |
595.8 |
106.6 |
399001.SZ |
1773.1 |
749.3 |
The experiment results (NN1: GRU, DATA SET: 399001.SZ).
Models |
Time |
RMSE |
MAPE |
MASE |
Dstat |
GRU |
0.090 |
127.056 |
1.143 |
1.048 |
0.621 |
GRU,GRU |
0.177 |
115.375 |
1.021 |
0.934 |
0.621 |
GRU,LSTM |
0.109 |
126.870 |
1.092 |
1.007 |
0.620 |
GRU,MLP |
0.023 |
120.234 |
1.050 |
0.961 |
0.615 |
The experiment results (NN1: LSTM, DATA SET: 000001.SH).
Models |
Time |
RMSE |
MAPE |
MASE |
Dstat |
LSTM |
0.102 |
27.243 |
0.643 |
1.204 |
0.653 |
LSTM,GRU |
0.188 |
22.195 |
0.481 |
0.900 |
0.673 |
LSTM,LSTM |
0.207 |
22.225 |
0.483 |
0.903 |
0.667 |
LSTM,MLP |
0.125 |
23.570 |
0.526 |
0.984 |
0.669 |
The experiment results (NN1: MLP, DATA SET: 000001.SH).
Models |
Time |
RMSE |
MAPE |
MASE |
Dstat |
MLP |
0.023 |
29.953 |
0.729 |
1.368 |
0.640 |
MLP,GRU |
0.114 |
23.964 |
0.521 |
0.973 |
0.665 |
MLP,LSTM |
0.128 |
23.328 |
0.509 |
0.950 |
0.658 |
MLP,MLP |
0.045 |
24.727 |
0.560 |
1.047 |
0.657 |