The GM (1, 1) forecast model fits in with mainly the smooth data sequence.
灰色预测模型GM(1,1)主要适用于光滑数据序列的预测。
Its fitting precision is related to the smooth degree of modeling data sequence.
它的拟合精度与建模数据序列的光滑度有关。
The accuracy of grey forecasting model (GM) is increased by improving the smooth degree of original data sequence.
用提高原始数据列光滑度的方法来提高灰色预测模型(GM模型)的精度。
The MZM is driven by a non-return-to-zero (NRZ) data sequence and biased at the nonlinear point to generate edge-triggered pulses.
利用双输出口调制器典型的互补输出特性，通过偏置调制器于非线性传输点，可得到一对由NRZ序列边沿触发的明暗脉冲。
Based on the characteristic of GM (1, 1) model, this paper presents a new modelling method for the model through adding a datum to the beginning of raw data sequence.
根据GM(1,1)模型的特点，通过在原始数据序列前加一个数的方式，提出了建立GM(1,1)模型的一种新方法。
A new method for precursory information extraction, i. e. slope difference information method is proposed in the paper for the daily mean value precursory data sequence.
针对日均值前兆数据序列，提出一种新的前兆信息提取方法，即斜率差信息法。
This paper presents the residual error forecast model of average-growing function by using its residual error data sequence to adjust the model based on the finished forecast model.
在均生函预报模型的基础上，利用其残差数据序列对均生函数预报模型进行校正，提出了均生函数残差预报模型。
Based on the data sequence of oil unit operation cost from 1999 to 2005 in Talimu oilfield, yearly operation cost prediction is conducted successfully by using GM (1, 1) grey model.
该文以塔里木油田1999年到2005年的原油单位操作成本数据序列为依据，利用GM(1,1)灰色模型，进行了年度操作成本预测，并得到了较好的结果。
The essence of the gray prediction model is to fit the known data sequence with an exponent form to a curve, and then extend the curve to the future in order to predict the unknown data.
灰色系统预测模型的本质是对已知数据序列进行类型为指数形式的曲线拟合，然后将此曲线延伸到未来，由此对未知的数据做出预测。
Markov Chain is suitable for short-term forecast of great capacity sample data sequence, but gray system forecast method is suitable for medium-term forecast of few capacity sample data sequence.
马尔柯夫链适用于大样本数据序列的短期预测，而灰色系统预测方法适用于小样本数据的中期预测。
Markov Chain is suitable for short-term forecast of great capacity sample data sequence, but gray system forecast method is suitable for medium-term forecast of few capacity sample data sequence.
马尔柯夫链适用于大样本数据序列的短期预测，而灰色系统预测方法适用于小样本数据的中期预测。
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