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

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29.Click the down arrow beside the Table Items button.
A list of fields appears.
29、点击Table Items，显示其下拉清单

30.Uncheck all the items except for Rank.
30、除了排名以外全部不选择。

31.Click OK.
31、点击ok

The methods table changes to show one parameter: Rank. Look at the Average Temperature data. Under Rank, Single Exponential Smoothing is highlighted in blue, bold text to show that it was used to generate the results. Seasonal Additive, originally the best, is still listed with a Rank equal to 1.
方法列表显示其中一个参数的排名情况，查看平均温度数据，根据排名，单边指数平滑是最高的，用蓝色表示，粗体字表示过去一般情况下的结果。周期附加，最初最好的，是被列在排名第一的位置。

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32.Move the Methods button to the left of the Series button.
32、将方法按钮移动到序列按钮的左边

The PivotTable reorganizes to show all the series grouped by method type as shown in Figure 1.26.
在图1.26中所有的序列以方法类别的方法进行显示，生成数据透析图。

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Chapter 2              
Understanding the Terminology
理解相关术语
In this chapter在本章中
•Forecasting                                                  •Multiple linear regression
预测                                                                多元线性回归
•Time-series forecasting                                 •Regression methods
时间序列预测                                                    回归方法
•Time-series forecasting error measures          •Regression statistics
时间序列预测误差度量                                         回归统计
•Time-series forecasting techniques                •Historical data statistics
时间序列预测技术                                              历史数据统计  
•Time-series forecasting statistics
时间序列预测统计

This chapter describes forecasting terminology. It defines the time-series forecasting methods that CB Predictor uses, as well as other forecasting-related terminology.
本章对预测术语进行了描述，对CB Predictor工具中时间序列预测方法已经与预测相关的术语进行了定义。

This chapter also describes the statistics the program generates and the techniques that CB Predictor uses to do the calculations and select the best-fitting method.
本章同时也对统计数据生成的程序和方法进行了描述，CB Predictor使用这些计算公式选择最佳的方法。


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Forecasting预测
Forecasting refers to the act of predicting the future, usually for purposes of planning and managing resources. There are many scientific approaches to forecasting. You can perform “what-if” forecasting by creating a model and simulating outcomes, as with Crystal Ball, or by collecting data over a period of time and analyzing the trends and patterns. CB Predictor uses the latter concept, analyzing the patterns of a time series to forecast future data.
预测是指对未来行为的预测，通常的目的是进行规划和管理资源。有许多科学的预测方法。你可以使用CB Predictor方法通过建立模型和模拟结果来进行“假设”预测，或者通过收集一段时间的时间数据，分析其趋势和结构。CB Predictor使用最新的概念和分析方法对时间序列结构，从而预测未来的数据。

The scientific approaches to forecasting usually fall into one of several categories:
科学的预测方法通常分为以下几类：

Time-series  Performs time-series analysis on past patterns of data to forecast results. This works best for stable situations where conditions are expected to remain the same.
时间序列   对以前数据用时间序列的方法分析结构来预测未来的结果。这个方法最适合用于预计未来将不会发生变化的稳定的情况。

Regression  Forecasts results using past relationships between a variable of interest and several other variables that might influence it. This works best for situations where you need to identify the different effects of different variables. This category includes multiple linear regression.
回归       根据因变量与会对其有影响的自变量因素之间的关系进行预测。这个方法最适合用于你需要确认不用因素分别有什么不同的作用的情
             况。回归方法包括多元线性回归。

Simulation  Randomly generates many different scenarios for a model to forecast the possible outcomes. This method works best where you might not have historical data, but you can build the model of your situation to analyze its behavior.
模拟       模拟是指在对未来可能的结果进行预测时会随机产生许多不同的情况形成一个模型。这种方法最适合用于你有可能没有历史数据但  
             是你需要建立一个模型对事情的情况进行分析。

Qualitative  Uses subjective judgment and expert opinion to forecast results. These methods work best for situations for which there are no historical data or models available.
定性       通过主观和专家的观点对预测结果进行分析。这种方法最适合用于没有历史数据和模型的情况。      

CB Predictor uses both time-series and multiple linear regression for forecasting. Crystal Ball uses simulation. Each technique and method has advantages and disadvantages for particular types of data, so often you might forecast your data using several methods and then select the method that yields the best results.
CB Predictor使用时间序列和多元线性回归的方法进行预测，水晶球使用模型。对于不通类型的数据。每种技术方法都有它的优点和缺点，所以一般情况下会使用多种方法来进行预测，并从中选择能产生最佳效果的其中一种方法

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Time-series forecasting时间序列预测

Time-series forecasting assumes historical data is a combination of a pattern and some random error. Its goal is to isolate the pattern from the error by understanding the pattern’s level, trend, and seasonality. You can then measure the error using a statistical measurement to describe both how well a pattern reproduces historical data and to estimate how accurately it forecasts the data into the future. For more information on these error measurements, see “Time-series forecasting error measures” on page 42.
时间序列预测是假设历史数据是根据一定结构组合起来的，并且误差是随机的。它的目的是通过数据结构的水平、趋势和季节性将其中的误差寻找出来。你可以利用统计度量的方法对误差进行度量，从而对历史数据的结构进行复制，对未来预测数据的准确性进行描述。关于误差度量的更多的内容，可以看第42页的“时间序列预测误差度量”。

When you select different forecasting methods from the Methods Gallery, CB Predictor tries all of them. It then ranks them according to which method has the lowest error, depending on the error measure selected in the Advanced dialog. The method with the lowest error is the best method.
当你从概率分布图中选择不同的预测方法时，CB Predictor会对他们都进行尝试，然后根据Advanced对话框中误差度量选择的哪种方法的误差最小进行排名。误差最小的方法就是最好的方法。

There are two primary techniques of time-series forecasting used in CB Predictor. They are:
有两种主要时间序列预测的方法。它们是：

Nonseasonal smoothing  Estimates a trend by removing extreme data and reducing data randomness.
非季节性平滑         通过去除异常数据和降低数据随机性来对数据的趋势性进行评估的行为。

Seasonal smoothing    Combines smoothing data with an adjustment for seasonal behavior.
季节性平滑           结合平滑的数据进行调整的季节性行为。

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Nonseasonal smoothing methods非季节性平滑

Smoothing models attempt to forecast by removing extreme changes in past data. The following methods are available.
平滑模型试图将以前数据中的异常点去除后进行预测，以下是各种方法：

Single moving average一次移动平均

Smooths out historical data by averaging the last several periods and projecting the last average value forward. CB Predictor can automatically calculate the optimal number of periods to average, or you can select the number of periods to average.
剔除过去几期的平均数据和预测的未来的值的异常数据，CB Predictor会自动计算出最佳的若干期的平均，或者你可以选择若干期的平均数据。

This method is best for volatile data with no trend or seasonality. It results in a straight, flat-line forecast.
这种方法最适合用于没有趋势和周期性的易变的数据，它的结果是直线、平线的预测。


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Double moving average二次移动平均

Applies the moving average technique twice, once to the original data and then to the resulting single moving average data. This method then uses both sets of smoothed data to project forward. CB Predictor can automatically calculate the optimal number of periods to average, or you can select the number of periods to average.
使用两次移动平均技术，一次是对原始数据进行移动平均，第二次是对一次移动平均的数据的结果进行再一次的移动平均。这种方法是根据两次移动平均的数据进行评估。CB Predictor会自动计算出最佳的若干期的平均，或者你可以选择若干期的平均数据。

This method is best for historical data with a trend but no seasonality. It results in a straight, sloped-line forecast.
这种方法最适合用于有一定的趋势性但是没有周期性的历史数据。它的结果是直线、斜线的预测。

CB Predictor Note: For See Appendix B, “Time-Series Forecasting Method Formulas” for more information about the formulas CB Predictor uses for the following methods.
注意：附录B中“时间序列预测方法公式”，可以详细了解公式CB Predictor使用下列方法。


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Single exponential smoothing (SES)单边指数平滑(SES）

Weights all of the past data with exponentially decreasing weights going into the past. In other words, usually the more recent data have greater weight. This largely overcomes the limitations of moving averages or percentage change models. CB Predictor can automatically calculate the optimal smoothing constant, or you can manually define the smoothing constant.
对以前的数据的权重用指数降低的方法进行加权，换句话说，就是越近期的数据的分量越重。这在很大程度上克服了移动平均和百分比变化模型的局限。CB Predictor可以自动计算平滑指数的最佳情况，或者你可以手动定义平滑指数。

This method is best for volatile data with no trend or seasonality. It results in a straight, flat-line forecast.
这种方法最适合用于有一定的趋势性但是没有周期性的历史数据。它的结果是直线、水平线的预测。


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Holt’s double exponential smoothing (DES)霍尔特双边指数平滑（DES）

Double exponential smoothing applies SES twice, once to the original data and then to the resulting SES data. CB Predictor uses Holt’s method for double exponential smoothing, which can use a different parameter for the second application of the SES equation. CB Predictor can automatically calculate the optimal smoothing constants, or you can manually define the smoothing constants.
双边指数平滑是单边指数平滑进行两次，第一次是对原始数据进行指数平滑，第二次是对单边指数平滑的数据再进行指数平滑。CB Predictor在双边指数平滑时使用霍尔特方法，这种方法是第二次进行指数平滑时，与单边指数平滑公式的参数是不一样的。CB Predictor会自动计算最佳的平滑指数，或者你也可以选择自定义平滑指数。

This method is best for data with a trend but no seasonality. It results in a straight, sloped-line forecast.
这种方法最适合用于有趋势但是没有季节性的历史数据。它的结果是直线，斜线的预测。


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Nonseasonal smoothing parameters非季节性平滑参数

There are several smoothing parameters used by the nonseasonal methods. For the moving average methods, the formulas use one parameter: period. When performing a moving average, you average over a number of periods. For single moving average, the number of periods can be any whole number between 1 and half the number of data points. For double moving average, the number of periods can be any whole number between 2 and one-third the number of data points.
使用非季节性方法有许多平滑参数。移动平均的方法，公式中有一个参数：周期。当你使用一个移动平均，你对一段时间的数据进行了平均。一次移动平均中，周期数可以是1到数据点一半数量的任何数。对于二次移动平均，周期数可以是2到数据点三分之一数量的任何数。

For single exponential smoothing, there is one parameter: alpha. Alpha (α) is the smoothing constant. The value of alpha can be any number between 0 and 1, not inclusive.
对单边指数平滑，有一个参数：平滑指数Alpha（α）。α的值可以是0到1之间除了0和1的的任何值。

For Holt’s double exponential smoothing, there are two parameters: alpha and beta. Alpha is the same smoothing constant as described above for single exponential smoothing. Beta (β) is also a smoothing constant exactly like alpha except that it is used during second smoothing. The value of beta can be any number between 0 and 1, not inclusive.
对于霍尔特双边指数平滑，有两个参数：alpha and beta，alpha和单边指数平滑描述的平滑指数一样， beta（β）也是和alpha一样的一个平滑指数，但是它是用在二次平滑中，这个值可以是0到1之间除了0和1的的任何值。

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明天说季节性平滑的相关方法

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