【连载】CB Predictor操作手册(Crystal Ball Predictor 水晶球预测)

qicai2002

谢谢分享，很需要啊 ！

lanj

When you select any one cell in the data range before you start the wizard, CB Predictor’s Intelligent Input guesses:
当你选择了数据表格中的任意一个单元格后再开始向导，CB Predictor会自动选择输入的范围：

•Your data series (in this case, A3:B42)
你的数据系列是在A3:B42的范围内

•Whether your data are in columns or rows
数据是按列或者按行排列

•Whether you have headers at the beginning of your data
你是否需要在开头开始数据

•Whether your first column or row contains dates or time periods
在第一列或第一行中是否包括数据活时间周期

5.Click Next.
5、点击Next

The Data Attributes tab appears as shown in Figure 1.3.
数据会像图1.3一样显示在向导卡中


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6.Under Step 4:
a. Select “weeks” from the Data Is In list.
b. Set the data to have no seasonality.
You have less than two complete seasons (cycles) of data, so cannot use seasonality.
6、第四步：
   a、在Data is in表格中选择“周”
   b、选择没有周期性
   如果选择周期性的话，你必须要有两个以上的完整周期的数据。

7.Under Step 5, make sure that Use Multiple Linear Regression is not checked.
You did not choose regression because you have only one series of data, so there are no dependencies between series requiring regression.
7、第五步，确认没有选择多元线性回归分析
如果只有一组数据的话，不需要选择线性回归，

8.Click Next.
8、点Next
The Method Gallery tab appears as shown in Figure 1.4.
图1.4显示了有哪些统计方法：


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9.Click Select All.
This selects all the time-series forecasting methods, but CB Predictor doesn’t use the seasonal methods, since you indicated that your data were not seasonal.
CB Predictor forecasts your values using each of the selected methods and ranks them according to how well they fit the historical data. CB Predictor uses the seasonal methods as well as the nonseasonal methods if you indicate on the Data Attributes tab that your data series have seasonality.
9、点击“select all”
选择了所有的时间序列的方法，但是如果你的数据没有周期性，CB Predictor不使用周期性方法。
CB Predictor使用每个选择的方法来预测数值，并将产生预测值的最佳方法进行排序，如何你的数据系在数据分布图中显示的是有周期性的，那么CB Predictor也会选择周期性的方法，而不是非周期性的。

10.Click Next.
The Results tab appears as shown in Figure 1.5. The only output selected by default is Paste Forecast, which adds the forecasted values to the end of your historical datas shown in Figure 1.5.
10、点Next
在图1.5中显示了结果的图表，唯一输出的结果是粘贴预测，这种预测方法会将预测值粘贴到历史数据的最后面。


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漂在生活

每天等更新……楼主加油哦！

lanj

11.Under Step 7, forecast the weekly sales for the rest of the year by entering 13 in the field.
11、第七步，在表格中填入需要预测的今年剩下的时间的销售量的周数，输入13周。

12.Click Preview.
The Preview Forecast dialog appears. It presents a graph with historical data, fitted data, forecast values, and confidence intervals as shown in Figure 1.6.
12、点击Preview
Preview Forecast对话框出现，这张图表中显示了历史数据、合适的数据、预测数据和置信区间的范围。





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lanj

13.In the Preview Forecast dialog, click the Method field.
The field lists all the methods CB Predictor tried, in order from the best-fitting method (designated by the word “Best”) to the worst-fitting method. CB Predictor calculates the forecasted values from the method that best fits the historical data. In this case, the method is Double Exponential Smoothing.
13、在Preview Forecast对话框中点击Method field。
在fiels清单中有CB Predcitor所有的预测方法，按照最佳到最差的方法顺序进行排列。CB Predictor从最佳的方法中历史数据情况计算出预测值，该图中最佳的方法是双边指数平滑。

The forecasted values appear as a blue line extending to the right of the historical data (green) and the fitted values also in blue). Above and below the forecasted values is the confidence interval (in red), showing the 5th and 95th percentiles of the forecasted values.
绿色的线代表的是历史数据，历史数据的最佳值对应的是与历史数据重合的蓝色的线条，在边上的蓝色线条代表的是预测值的趋势情况。在预测值的蓝色线条上下的红色线条是置信区间的范围，即预测值的5%-95%的可能性。

14.Click Run.
The program pastes the forecasted values at the end of the historical data (in bold), extending the date series as well. The forecasted values were forecasted using the best method, as shown in the Preview dialog.
Figure 1.7 Pasted shampoo sales values
14、点击Run
在历史数据后面粘贴预测值，该预测值是在Preview Forecast图中显示的最佳的方法得到的。


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lanj

Based on the results, you complete your memo to upper management. Current strategies seem to be working so you recommend funding another project instead.
在这个结果的基础上，你根据上级管理层的生成备忘录，你应该建议改变策略，投资其他的产品。


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lanj

以上是用CB Predictor进行一个简单的销售量的预测，明天会将CB Predictor如何进行工作的相关内容与大家分享

lanj

How CB Predictor works CB Predictor如何工作？

Most historical or time-based data contain some kind of underlying trend or seasonal pattern. However, most historical data also contain random fluctuations — “noise”— that make it difficult to detect these trends and patterns without using a computer. CB Predictor uses sophisticated time-series methods to analyze the underlying structure of your data. It then projects the trends and patterns to predict future values.
大部分的历史或时间基础的数据都包含了相关的趋势和周期性，但是有些历史数据也是随机的，如果不借助电脑很难找到它的趋势性，CB Predictor使用时间序列趋势的图表方法对数据的结构进行分析，从而得到预测值的趋势和模型。

When you run CB Predictor, it tries each time-series method on your data and calculates a mathematical measure of goodness-of-fit. CB Predictor selects the method with the best goodness-of-fit as the method that will yield the most accurate forecast. CB Predictor performs this selection automatically for you, but you can also select individual methods manually or override the method CB Predictor recommends with a different one.
当你运行CB Predictor，它对历史数据运用各种时间序列的方法进行计算，得到一个最佳的数学度量。CB Predictor选择最佳的方法从而得到最精确的预测值，CB Predictor将为你自动选择最佳的方法，当然你可以根据自己的要求在方法图表中选择其他的。

The final forecast shows the most likely continuation of your data. Keep in mind that all these methods assume that some aspects of the historical trend or pattern will continue into the future. However, the farther out you forecast, the higher the likelihood that events will diverge from past behavior, and the less confident you can be of the results. To help you gauge the reliability of your forecast, CB Predictor provides a confidence interval indicating the degree of uncertainty around your forecast.
在你的数据上显示了最有可能的预测值，要记住，这些方法都是假定历史数据的趋势和结构将会延续到将来。但是，预测的值的时间越长，就会越有可能偏离以前的行为，预测的可信性也就越低。为了保证预测值的可靠性，CB Predictor提供了一个置信区间，通过这个置信区间来表示预测值的不可信度。

The final step of forecasting involves interpreting the results and integrating them into your decision-making process. CB Predictor provides comprehensive output to assist you in this process. You can request detailed reports, customizable Excel charts and graphs, and summary PivotTables. CB Predictor can also automatically create Crystal Ball assumptions for each forecasted value, letting you integrate risk analysis into the process.
在预测最后一步是解释结果，并将结果整合到您的决策过程中。文件提供全面的销售量预估，可以帮助您完成这一过程。您可以要求得出详细的报告，可定制的Excel图表和图表，数据透视表和汇总。CB Predictor还可以自动为每个水晶球预测值进行假设，帮你将入风险分析也考虑进来。


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Toledo Gas tutorial托莱多燃气公司案例

Suppose you work for Toledo Gas Company in the Residential Division. The Public Utilities Commission requires you to predict the gas usage for the coming year to make sure that the company can meet the demand.
假设你在托莱多住宅天然气公司工作，现在公共事业委员会要求你对明年的燃气使用量进行预测，以确保公司能够满足市民的需求。

To start the tutorial: 案例开始：

1.In Excel with Crystal Ball loaded, open the Toledo Gas spreadsheet, Toledo Gas.xls.
To find this example, select Help > Crystal Ball > Crystal Ball Examples and locate it in the list of CB Predictor examples, or browse to its folder. By default, the file is stored in this folder:
C:\Program Files\Decisioneering\Crystal Ball 7\Examples\CB Predictor Examples.
1、打开托莱多天然气电子表格，默认的存放位置是：C:\ProgramFiles\Decisioneering\Crystal Ball 7\Examples\CB Predictor Examples.

When you double-click the link or the file, the Toledo Gas spreadsheet appears in Excel.
双击文件夹后，托莱多燃气公司的数据表会打开


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2.Select cell C5.
2、选择单元格C5

3.Choose Run > CB Predictor.
The Input Data tab appears. CB Predictor’s Intelligent Input feature selects all the data from cell B4 to cell F100.
3、选择Run>CB Predictor
输入数据的表格出现，CB Predictor自动输入所有的数据，从B4：F100。


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4.Click Next.
The Data Attributes tab appears
4、点Next
出现数据分布对话框

5.Confirm that in Step 4, settings are “Data is in months with seasonality of 12 months.”

5、检查第四步，设置数据输入的周期性是12个月。

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Using regression使用回归
Through research, you know that your residential gas usage is primarily affected by three variables: new home starts, the temperature, and the price of natural gas. However, you aren’t sure how much effect each has on gas usage.
通过调查，可以知道住在天然气使用量是收到三个方面因素的影响：新房的数量、问题和天然气的价格。但是你不能确认天然气是使用效果是多大？

Because you have independent variables affecting a dependent variable (the variable that you are interested in), this forecast requires regression. For regression, CB Predictor uses a technique called HyperCasting™, which in one easy step:
因为对因变量影响的有多个自变量，预测的结果需要进行回归。用回归的方法，CB Predictor使用一种叫做HyperCasting的技术：

a. Creates an equation that defines the mathematical relationship between the independent variables and your dependent variable.
a、根据因变量和自变量之间的关系建立一个数学方程式。

b. Forecasts each independent variable using time-series forecasting methods.
b、使用时间序列预测方法预测每一个因变量

c. Uses the equation it created in the first step, combining the forecasted independent variable values, to create the forecast for the dependent variable.
c、使用第一步创建的方程式，结合自变量的预测值，生成因变量的预测值。

In the Toledo Gas spreadsheet, the dependent variable is the historical residential gas usage. The independent variables are:
•Number of occupancy permits issued (new housing completions)
•Average temperature per month
•Unit cost of natural gas
在托莱多天然气电子表格中，因变量是居民天然气使用量的数据，自变量是：
        允许使用的数量（新房的居住率）
        每个月的平均气温
        天然气的单位成本
To resume your tutorial:

6.Under Step 5, select Use Multiple Linear Regression.
6、在第五步之后，选择多元线性回归。
The Regression Variables dialog appears.
回归变量的对话框显示出来


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7.Confirm that Usage appears in the Dependent Variables list.
If it does not:
•Select Usage in the All Series list.
•Click >> next to the Dependent Variables list.
The Usage variable moves to the Dependent Variables list.
7、确认在因变量列表中有“使用量”这个因素
如果没有：在所有系列列表中选择“使用量”，点击chick移到因变量列表中。

8.Confirm that Occupancy Permits, Average Temperature, and Cost Of Nature Gas Per Ccf appear in the Independent Variables list.
If they do not:
•Select Occupancy Permits, Average Temperature, and Cost Of Natural Gas Per Ccf in the All Series list.
•Click >> next to the Independent Variables list.
The variables move to the Independent Variables list.
8、确认允许使用的数量（新房的居住率）、每个月的平均气温、天然气的单位成本是否在自变量列表中。
如果没有，选择自变量，移到自变量列表中。

9.Click OK.
The Data Attributes tab reappears.
9、点击OK。出现数据分布图对话框

10.Click Next.
The Method Gallery tab appears.
10、点解Next。出现概率分布图对话框。

11.Click Next again.
The Results tab appears.
11、点击Next。出现结果。

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