介绍了单整自回归移动平均模型(antoregressive integrated moving average model,ARIMA)及其建模思路,并结合Eviews软件将ARIMA模型应用于成都市年用电量的分析与预测。经检验此模型预测精度较高,拟合效果理想,体现了应用ARIMA模型进...介绍了单整自回归移动平均模型(antoregressive integrated moving average model,ARIMA)及其建模思路,并结合Eviews软件将ARIMA模型应用于成都市年用电量的分析与预测。经检验此模型预测精度较高,拟合效果理想,体现了应用ARIMA模型进行用电量预测的可行性,可以为电力系统工作人员进行年用电量预测提供参考。展开更多
The amino acid composition and the biased auto-correlation function are considered as features, BP neural network algorithm is used to synthesize these features. The prediction accuracy of this method is verified by u...The amino acid composition and the biased auto-correlation function are considered as features, BP neural network algorithm is used to synthesize these features. The prediction accuracy of this method is verified by using the independent non-homologous protein database. It is shown that the average absolute errors for resubstitution test are 0.070 and 0.068 with the standard deviations 0.049 and 0.047 for the prediction of the content of α-helix and β-sheet respectively. For cross-validation test, the average absolute errors are 0.075 and 0.070 with the standard deviations 0.050 and 0.049 for the prediction of the content of α-helix and β-sheet respectively. Compared with the other methods currently available, the BP neural network method combined with the amino acid composition and the biased auto-correlation function features can effectively improve the prediction accuracy.展开更多
文摘介绍了单整自回归移动平均模型(antoregressive integrated moving average model,ARIMA)及其建模思路,并结合Eviews软件将ARIMA模型应用于成都市年用电量的分析与预测。经检验此模型预测精度较高,拟合效果理想,体现了应用ARIMA模型进行用电量预测的可行性,可以为电力系统工作人员进行年用电量预测提供参考。
文摘The amino acid composition and the biased auto-correlation function are considered as features, BP neural network algorithm is used to synthesize these features. The prediction accuracy of this method is verified by using the independent non-homologous protein database. It is shown that the average absolute errors for resubstitution test are 0.070 and 0.068 with the standard deviations 0.049 and 0.047 for the prediction of the content of α-helix and β-sheet respectively. For cross-validation test, the average absolute errors are 0.075 and 0.070 with the standard deviations 0.050 and 0.049 for the prediction of the content of α-helix and β-sheet respectively. Compared with the other methods currently available, the BP neural network method combined with the amino acid composition and the biased auto-correlation function features can effectively improve the prediction accuracy.
