Fits –Simulation-4

Scale with common range（缩放统一范围）：为FIR，F2P和PAR仿真数据（包括取决于选中活动单选按钮的仿真、误差，差分和去趋势数据）设置相同的范围（范围=最大值—最小值）。若要激活此选项，请右键单击与FIR，F2P或PAR数据对应的图例行。
Trend Properties（趋势属性）：自定义绘图属性（如背景颜色，字体，样式）。
Create Tag From Prediction（从预测创建位号）：从来自FIR，F2P和PAR模型的仿真、误差、差分和去趋势数据创建位号。若要激活此选项，请右键单击与FIR，F2P或PAR数据对应的图例行。可使用此功能检查模型的质量。根据误差或残差创建位号，并检查Tag Groups （位号组）> Trend（趋势）菜单的自相关图。
理想的情况是在非零的滞后时间，模型残差的自相关图应该显示零或非常小的相关系数。类似的图显示残差具有随机（白噪声）特性，并且模型已经充分捕获来自输入变量的非随机（确定性）影响。
Make Prediction Tag User Visible（使预测位号用户可见）：将仿真数据（包括取决于选中活动单选按钮的仿真、误差，差分和去趋势数据）填充到工作区中的位号列表。
如下图所示，通常对于斜坡模型，使用simulation（仿真）选项将产生预测漂移：

在数据段起始时，拟合预测（青色）开始匹配原始时间序列（蓝色），但随后似乎随着时间开始漂移。这是AIDAPro在数据段的时间零点处将仿真预测与实际数据相匹配，而过程在该时间点处于非稳定状态的结果。在实际生产数据集中，这种情况会非常频繁地发生。
为了更好地评估这种情况下的模型拟合，请检查不同的显示框。当是斜坡输出变量时，为了匹配AIDAPro实际拟合，曲线会更新以显示输出变量和仿真预测的导数。如下图所示，设置差分标志可以更容易地验证斜坡输出变量拟合的质量：

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**Scale with common range: **Set the same range (range=max values-min values) for the simulated FIR, F2P, and PAR data (this includes the simulation, error, difference, and detrend data of depending on the activated radio button). To activate this option, right click on a legend row that corresponds to FIR, F2P, or PAR data.
**Trend Properties: **Customize the plot properties (e.g background color, font, styles).
Create Tag From Prediction：Creates a tag from simulation, error, difference, and detrend data from FIR, F2P, and PAR models. To activate this option, right click on a particular legend row that corresponds to FIR, F2P, or PAR data. Use this functionality to check the quality of your model. Create a tag from errors or residuals and check the autocorrelation plots from Tag Groups > Trend menu.
The autocorrelation plots of modeling residuals ideally should show zero or very small correlation coefficients at time lags other than zero. Such plot implies that the residual has random (white noise) characteristics and non-random (deterministic) effects from input variables have been sufficiently captured in the model.
Make Prediction Tag User Visible：Populates the simulated data (simulation, error, difference, detrend data depending on the activated radio button) to the list of tags in the workspace.
Often for ramp models, using the simulation option results in drifting predictions as seen in the following graph:
The fit prediction (cyan) matches the original time series (blue) well at the beginning of the segment, but then seems to drift away over time. This is a result of AIDAPro matching the simulation prediction to the actual data at time zero of the segment, and the process not being at steady state at that point in time. This situation can happen very frequently in real-world data sets.
To better assess model fits in this type of situation, check the difference box on the display. The plot is updated to show the derivatives of the output variable and the simulation prediction, which matches the actual fit done by AIDAPro in the case of ramp output variables. As seen in the following figure, visualizing the quality of a ramp output variable fit is much easier with the difference flag set:

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2016.11.25