现有最小二乘支持向量机回归的训练和模型输出的计算需要较长的时间，不适合在线实时训练。
Least square support vector machines regression without sparsity needs longer training time currently, and is not adapted to online real-time training.
最小二乘支持向量机回归预测对训练样本数据区间内的预测精度很高，但是对前向外推预测效果不是很好；
RBF neural network is applied to time series forecast with the same data in order to compare the forecast effect with LS-SVM model.
结果显示，把最小二乘支持向量机回归预测与等步长时序预测相结合的预测方法应用于地下工程围岩位移监测数据的分析及预测是可行的；
Combining the advantages of regression analysis methods and time series forecast model with equal step length, a compound forecasting model was set up , and was tested with engineering data.
比较分析了最小二乘支持向量机（LSSVM）和广义回归神经网络（GRNN）这两种方法的特点。
The features of two methods, i. e. least square support vector machine (LSSVM) and generalized regression neural network (GRNN) are compared and analyzed.
比较分析了最小二乘支持向量机（LSSVM）和广义回归神经网络（GRNN）这两种方法的特点。
The features of two methods, i. e. least square support vector machine (LSSVM) and generalized regression neural network (GRNN) are compared and analyzed.
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