Introducing Oracle Crystal Ball Predictor: a new approach to forecasting in MS Excel Environment

Transcription

1 <Insert Picture Here> Introducing Oracle Crystal Ball Predictor: a new approach to forecasting in MS Excel Environment Samik Raychaudhuri, Ph. D. Principal Member of Technical Staff ISF 2010

2 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-series forecasting capabilities The current version finally provides much-needed attention to the forecasting capabilities by introducing an array of features to the tool

3 Oracle Crystal Ball Predictor Features The time-series forecasting tool in Oracle Crystal Ball is called Predictor. We will refer to it henceforth as CB Predictor The tool has an interesting set of usability features which sets it apart from 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 Excel 2010 will be supported soon Usual analytical features available (details follow) Ease of use, with non-intimidating dialogs and sensible defaults for the uninitiated and providing array of configuration options for power users Professional forecasting charts and reports Seamlessly integrates with Monte Carlo simulation for conducting risk analysis along with time-series forecasting Can definitely be the forecasting software of choice for the rest of us!!

4 CB Predictor Analytical Feature List CB Predictor has an extensive list of features designed to make the forecasting experience easy and productive The tool sports a wizard like interface to guide users through the forecasting process The features can be broadly subdivided into two categories: Data preparation and forecasting Result analysis and reporting

5 CB Predictor Analytical Feature List: Data Preparation and Forecasting Data preparation and forecasting is performed over four feature-rich screens Identifying input data Describing data characteristics Selecting models to run Choosing forecasting options

6 Data Preparation and Forecasting: The Welcome Screen CB Predictor starts off at the Welcome screen Talks about the basic forecasting procedure

7 Data Preparation and Forecasting: Identifying Input Data

8 Data Preparation and Forecasting: Identifying Input Data (Contd.) The hidden features Intelligent data selection Select one cell in a contiguous range of series and start CB 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 in an year, dates, or quarters etc.

9 Data Preparation and Forecasting: Describing Data Characteristics [Planned Screen] Seasonality detection Events modeling (New) Missing value imputation Outlier detection

10 Data Preparation and Forecasting: Describing Data Characteristics (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 and their probabilities at various lags in the data Has been tested extensively on M1-competition and M3-competition data

11 Data Preparation and Forecasting: Describing Data Characteristics (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 each forecasting 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

12 Data Preparation and Forecasting: Describing Data Characteristics (Contd.) Events modeling Still in the works, slated for a future release

13 Data Preparation and Forecasting: Selecting Forecasting Models [Planned Screen]

14 Data Preparation and Forecasting: Selecting Forecasting 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 automatically detected or can be overridden by the user

15 Data Preparation and Forecasting: Selecting Forecasting Models (Contd.) Multiple Linear Regression Supports lagged dependent variables Supports multiple dependent variables Stepwise regression (forward and iterative) for choosing important independent variables from a pool Performs automatic forecasting of the dependent variable using regression equation and forecasts from independent variable ARIMA (New) Still in the works, slated for a future release

16 Data Preparation and Forecasting: Forecasting Options

17 Features: CB Predictor Analytical Feature List: Result Analysis and Reporting Displays forecast and confidence intervals Displays the best method for each series with easy browsing for other series and methods Important statistics for each method Seasonal bands for visual identification of patterns in the historical and forecasting horizon Adjust forecasting horizon and CI on-the-fly

18 CB Predictor Analytical Feature List: Result Analysis and Reporting (Contd.) Manual adjustment of forecasts Supports various type of adjustments

19 CB Predictor Analytical Feature List: Result Analysis and Reporting (Contd.) Access to reports and data extraction from the results window menu Integration with CB Monte Carlo simulation Forecasts are treated as normal probability distributions with forecast value as the mean and standard error as the standard deviation Can then be used for risk analysis

20 Example: Using CB Predictor for Forecasting and Risk Analysis: Monica s Bakery A rapidly-growing boutique bakery in Taos, New Mexico The owner, Monica, has kept records of sales of her three main products: French bread, Italian bread and pizza Wants to analyze the cash flow of the business to purchase fixed assets

21 Example: Monica s Bakery (Contd.) Demonstration Reports

22 Example: Monica s Bakery (Contd.) Static analysis shows: Doesn t look good as per the minimum cash target goes But is it really the true picture? Risk analysis thinking in ranges

23 Example: Monica s 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 Let s try to analyze the uncertainty in some of these variables and see the interaction effect Set probability distributions on top of input variables (called assumptions) 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

24 Example: Monica s Bakery (Contd.) Use the forecast charts to find out the probability of hitting the minimum cash targets Probability for July: 65.37% Probability for August: 30.97% Probability for September: 97.17%

25 Example: Monica s Bakery (Contd.) How about considering the uncertainty in the forecast values themselves? 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 forecasting method, which has a reasonably narrow CI Choose another forecasting methods having wider forecast CI s and verify that the risk spread increases for target variables

26 Questions? Presenter: Samik Raychaudhuri