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Introducing Oracle Crystal Ball Predictor: a new approach toforecasting in MS Excel Environment
Samik Raychaudhuri, Ph. D.Principal Member of Technical Staff
ISF 2010
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Oracle Crystal Ball
An add-in to Microsoft Excel for performing:
Monte Carlo simulation
Stochastic optimization
Time series forecasting
The focus of the software has been historically on
Monte Carlo simulation, with basic time-seriesforecasting capabilities
The current version finally provides much-needed
attention to the forecasting capabilities by introducingan array of features to the tool
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Oracle Crystal Ball Predictor Features
The time-series forecasting tool in Oracle Crystal Ball is calledPredictor. We will refer to it henceforth as CB Predictor
The tool has an interesting set of usability features which sets it apartfrom other forecasting software:
Works completely in the familiar Microsoft Excel spreadsheet environment:no data import or result export required
Officially supported on MS Excel XP, 2003 and 2007. Excel 2010 will be
supported soon Usual analytical features available (details follow)
Ease of use, with non-intimidating dialogs and sensible defaults for theuninitiated and providing array of configuration options for power users
Professional forecasting charts and reports
Seamlessly integrates with Monte Carlo simulation for conducting riskanalysis along with time-series forecasting
Can definitely be the forecasting software of choice for the rest of us !!
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CB Predictor Analytical Feature List
CB Predictor has an extensive list of featuresdesigned to make the forecasting experience easy
and productive
The tool sports a wizard like interface to guide usersthrough the forecasting process
The features can be broadly subdivided into twocategories:
Data preparation and forecasting
Result analysis and reporting
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CB Predictor Analytical Feature List: DataPreparation and Forecasting
Data preparation and forecasting is performed overfour feature-rich screens
Identifying input data
Describing data characteristics
Selecting models to run
Choosing forecasting options
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Data Preparation and Forecasting: The WelcomeScreen
CB Predictor starts off at the Welcome screen
Talks about the basic forecasting procedure
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Data Preparation and Forecasting: Identifying InputData
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Data Preparation and Forecasting: Identifying InputData (Contd.)
The hidden features
Intelligent data selection
Select one cell in a contiguous range of series and startCB Predictor
Identifies the complete range of data, orientation of the
data, and the position of header and date ranges if exist Supports discontinuous data range (e.g., alternate rows or
columns of data)
Supports logical aligning of data: pre-data gaps
Automatically identify various type of periods like months inan year, dates, or quarters etc.
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Data Preparation and Forecasting: Describing DataCharacteristics
[Planned Screen]
Seasonality detection
Events modeling (New)
Missing value imputationOutlier detection
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Data Preparation and Forecasting: Describing DataCharacteristics (Contd.)
Seasonality detection
We automatically detect seasonality for input series
One can override the seasonality of each series or set them to non-seasonal
The detection algorithm uses threshold-based analysis of autocorrelation andtheir probabilities at various lags in the data
Has been tested extensively on M1-competition and M3-competition data
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Data Preparation and Forecasting: Describing DataCharacteristics (Contd.)
Data screening
Missing value imputation: CB Predictor can
impute the missing values in the dataset Uses nearest neighbor interpolation or cubic
spline interpolation
Options to control the interpolation scheme
Outlier detection: detects outliers specific to eachforecasting method
Suggests replacing values
Options to control the method and the
aggressiveness of the detection algorithm In each case, charts are available to ease the
decision making process
Defaults work great for majority of scenarios
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Data Preparation and Forecasting: Describing DataCharacteristics (Contd.)
Events modeling
Still in the works, slated for a future release
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Data Preparation and Forecasting: SelectingForecasting Models
[Planned Screen]
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Data Preparation and Forecasting: SelectingForecasting Models (Contd.)
Non-seasonal Models Single moving average
Single exponential smoothing Double moving average
Double exponential smoothing
Seasonal Models Seasonal Additive
Seasonal Multiplicative
Holt-Winters Seasonal Additive
Holt-Winters Seasonal Multiplicative
Order and other parameters are automaticallydetected or can be overridden by the user
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Data Preparation and Forecasting: SelectingForecasting Models (Contd.)
Multiple Linear Regression
Supports lagged dependent variables
Supports multiple dependent variables
Stepwise regression (forward and iterative) for choosingimportant independent variables from a pool
Performs automatic forecasting of the dependent variableusing regression equation and forecasts from independentvariable
ARIMA (New)
Still in the works, slated for a future release
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Data Preparation and Forecasting: ForecastingOptions
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CB Predictor Analytical Feature List: ResultAnalysis and Reporting
Features:
Displays forecast andconfidence intervals
Displays the best methodfor each series with easybrowsing for other seriesand methods
Important statistics for each
method Seasonal bands for visual
identification of patterns inthe historical andforecasting horizon
Adjust forecasting horizonand CI on-the-fly
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CB Predictor Analytical Feature List: ResultAnalysis and Reporting (Contd.)
Manual adjustment of forecasts
Supports various type of adjustments
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CB Predictor Analytical Feature List: ResultAnalysis and Reporting (Contd.)
Access to reports and data extraction from the resultswindow menu
Integration with CB Monte Carlo simulation
Forecasts are treated as normal probability distributions withforecast value as the mean and standard error as the
standard deviation Can then be used for risk analysis
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Example: Using CB Predictor for Forecasting andRisk Analysis: Monicas Bakery
A rapidly-growing boutique bakery inTaos, New Mexico
The owner, Monica, has keptrecords of sales of her three mainproducts: French bread, Italian
bread and pizza Wants to analyze the cash flow of
the business to purchase fixed
assets
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Example: Monicas Bakery (Contd.)
Demonstration
Reports
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Example: Monicas Bakery (Contd.)
Static analysis shows:
Doesnt look good as per the minimum cash targetgoes
But is it really the true picture? Risk analysis thinking in ranges
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Example: Monicas Bakery (Contd.)
The risk analysis of the cash flow
Lot of uncertain variables in the spreadsheet model
The numbers really represent the likely scenario or the best guess scenario
Lets try to analyze the uncertainty in some of these variables and seethe interaction effect
Set probability distributions on top of input variables (calledassumptions)
COGS Overhead
Financing
Taxes
Set the target variables in which we are interested (called forecasts)
Run a Monte Carlo Simulation
5000 trials
Get the probability distribution for the target variables
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Example: Monicas Bakery (Contd.)
Use the forecast chartstofind out the probability of
hitting the minimum cashtargets
Probability for July: 65.37%
Probability for August:30.97%
Probability for September:97.17%
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Example: Monicas Bakery (Contd.)
How about considering the uncertainty in the forecast valuesthemselves?
Have those as assumptions as well New probability of hitting the minimum cash targets
Probability for July: 64.42%
Probability for August: 33.24%
Probability for September: 96.55%
These are when you have chosen to use the best forecastingmethod, which has a reasonably narrow CI
Choose another forecasting methods having wider forecast CIsand verify that the risk spread increases for target variables
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Questions?
Presenter:
Samik Raychaudhuri
mailto:[email protected]:[email protected]