In this paper, a new model system is introduced, which synthetically applies time series model, nonlinear regression and combination forecasting model to forecast the change of the market price.
文章通过对一套市场价格预测模型体系的介绍，综合运用时间序列模型、多元非线性回归和组合模型来预测市场价格走势，探索从多角度综合预测市场价格的问题。
Finally, the results show the methods can effectively come into being regression analysis model of time-series data streams, and fulfill the prediction of future data streams.
最后，试验分析展示了研究结果能够有效地产生时间序列数据流的回归模型和实现数据流未来数据的预测。
There are traditional model methods of forecasting short-term load, such as time series, regression analysis, and so on.
电力系统短期负荷预测使用的方法有传统建模方法，诸如时间序列、回归分析等方法。
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.
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；
The forewarning control limits and time series regression judgment model are built up based on the reliability series model in a non-repairable system and Shewhart control charts.
研究结果：利用不可修复可靠性串联模型与休哈特控制图建立预警控制界限，建立时间序列回归判别模型。
The time series ar (p) self-regression model is widely applied to prediction in economic field. Most of researchers and technicians in engineering are not familiar with it.
时间序列ar (P)自回归模型多应用于经济预测领域，工程技术界多数人对此不太热悉。
The theory of linear regression and the theory of moving average are applied to analyse single data in time series, the model of a linear moving self regression forecast are given out.
应用线性回归分析和移动平均理论，对按时间次序排列的单一数据序列，给出了一种线性移动自回归预测模型，并对原始数据受不确定因素影响而产生的随机振荡，给出了合理的控制区间和运行通道。
This paper introduces the time series AR (p) self-regression model. and how to apply it to predict the output of B. F. gas in Iron and Steel Works.
本文介绍了时间序列ar (P)自回归模型以及如何利用它来预测高炉煤气的产量。
The precision of mixed model considering plot random effects, time series error autocorrelation and different plantation density at one time is better than that of ordinary regression analysis method.
同时考虑样地的随机效应、观测数据的时间序列相关性及不同初植密度的混合模型模拟精度比传统的非线性回归方法模拟精度高。
The precision of mixed model considering plot random effects, time series error autocorrelation and different plantation density at one time is better than that of ordinary regression analysis method.
同时考虑样地的随机效应、观测数据的时间序列相关性及不同初植密度的混合模型模拟精度比传统的非线性回归方法模拟精度高。
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