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Forecasting Engine

Forecasting Engine. Project Members Kaushal Mittal Abhishek Seth Amar Agrawal Guide: Prof. Bernard Menezes Pankaj Gulhane Kalam Shah. Problem Definition.

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Forecasting Engine

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  1. Forecasting Engine Project Members Kaushal Mittal Abhishek Seth Amar Agrawal Guide: Prof. Bernard Menezes Pankaj Gulhane Kalam Shah

  2. Problem Definition • Develop a forecast engine that interacts with multiple forecasting back-ends such as SAS, MATLAB and user programs to generate useful forecasts for the given input series. • Provide support for using ensemble of experts in order to judge the performance of these experts at different instances of time series.

  3. Client • This project is useful for Academic/Industrial people, who are working on Forecasting of Time Series. • Present Clients • Pankaj Gulhane (MTech’03 KReSIT) • Kalam Shah (MTech’03 KReSIT)

  4. User Requirements • Must be able to talk to multiple backend forecasting systems (SAS, MATLAB). • Facilitate the addition of new systems/experts with minimum effort. • Use an ensemble of experts and combine their results in a meaningful way. • Support adaptive forecasting by using only the values in the past to forecast current observation point.

  5. Challenges • Various forecasting applications have • Different input / output formats • Different scripting language. • Lack of support for adaptive forecasting. • A simple and powerful interface required • Minimum human interactions. • Generic – addition of new forecasting system.

  6. Solutions • Providing a consistent interface to heterogeneous forecasting back ends by • Generating customized scripts for each backend. • Interpretation of different outputs from these back ends • Providing a consistent report to the user. • Abstracting these interface details from the user. • Addition of new experts • Template file support for defining the execution script for experts. • Populating template files through parameter-files to define experts.

  7. Solutions cont.. • Combining opinion of various experts • Analyzing the output from various experts collectively • Find the best n model for a given series. • Find the best n model for each observation • Adaptive forecasting • Writing specific scripts that use only past data to predict the next value. • Use the script repeatedly to get the forecast for each observation.

  8. Usage Scenario • Input • Time series • Ensemble of experts to use for the series. • Output • A report of best ‘k’ models for any of the following two scenarios. • Scenarios • Obtain the best forecasting models from the ensemble and use them to forecast on the series. • Fit multiple experts on the series and obtain the models that give the best predictions for each observation point in the series.

  9. Scenario 1 Sample Report Forecasts using the best 3 models. Summary Series Error Model Name 5.172482640050754 LINEAR_TREND 9.669957978203671 WINTERS_ADDITIVE 11.325417084252539 LINEAR_EXPO ObsId ActualValue ForecastValue Error Value ModelName 1 106922.0 108620.52 1.5885598847758218 LINEAR_TREND 106922.0 109083.75 2.0218009390022633 WINTERS_ADDITIVE 106922.0 124301.56 16.2544284618694 LINEAR_EXPO 2 130621.0 114201.32 12.570474885355337 LINEAR_TREND 130621.0 108651.4 16.81934757810766 WINTERS_ADDITIVE 130621.0 122540.29 6.1863789130384905 LINEAR_EXPO 3 125251.0 124769.27 0.3846116997069851 LINEAR_TREND 125251.0 113045.32 9.74497608801526 WINTERS_ADDITIVE 125251.0 125961.14 0.5669735171775071 LINEAR_EXPO 4 140318.0 130774.79 6.801130289770383 LINEAR_TREND 140318.0 115486.46 17.696617682692168 WINTERS_ADDITIVE 140318.0 127615.91 9.052359640245724 LINEAR_EXPO

  10. Scenario 2 Sample Report Forecasts using 3 best model at each Observation. Summary Series Error Model Name 3.34066242459444 Optimum 1 7.5991216842230385 Optimum 2 15.228073593689482 Optimum 3 ObsId Actual Value Forecast Value Error Value Model Name 1 106922.0 108620.52 1.5885598847758218 LINEAR_TREND 106922.0 109083.75 2.0218009390022633 WINTERS_ADDITIVE 106922.0 124301.56 16.2544284618694 LINEAR_EXPO 2 130621.0 122540.29 6.1863789130384905 LINEAR_EXPO 130621.0 114201.32 12.570474885355337 LINEAR_TREND 130621.0 108651.4 16.81934757810766 WINTERS_ADDITIVE 3 125251.0 124769.27 0.3846116997069851 LINEAR_TREND 125251.0 125961.14 0.5669735171775071 LINEAR_EXPO 125251.0 113045.32 9.74497608801526 WINTERS_ADDITIVE 4 140318.0 130774.79 6.801130289770383 LINEAR_TREND 140318.0 127615.91 9.052359640245724 LINEAR_EXPO 140318.0 115486.46 17.696617682692168 WINTERS_ADDITIVE

  11. Architecture & Design Forecasting Engine Script Executor Script Generator Output Extractor Report Generator SAS Forecast-Analyzer MATLAB

  12. Design Cont.. • Script Generator module • A single code generator for all systems. • Template files - A XML specification for defining the syntax of the application specific input script file. • This template file is used by the ScriptGenerator to generate customized input scripts. • Parameter files – specify the parameters. Code generator use it to customize the template file and generate script. • Main classes – CodeGenerator & ScriptExecutor.

  13. Design cont.. • Output Extractor • Factory pattern used. • Output Extractor extracts output from output files generated by backend systems. • Different for different forecasting systems. • User can add his own output extractor through this factory. • ModelMetrics - Store the results for each Expert. • Main Classes – OutputExtractorFactory, SASOutputExtractor, MatlabOutPutExtractor

  14. Use Case • How the users can use the system? • Use command line to give a list of experts (property file) and general arguments, system generates the report. • User writes his own java program using our class API’s to interact with backend systems. • Provides power to exploit the design to meet new emerging requirements. • Use the output generated for further processing.

  15. User Interface::Command Line • Java stats.metric.ForecastEngine [options] • Mandatory options • -I <input file> ( data series) • -p <property file> ( list of experts to be used) • -r <report file> • System generates two reports <report file>.best and <report file>.opt • Optional parameters • -t <count> number of best experts in report. (3) • -s <count> forecasting starts from this observation (31) • -f <count> forecast upto this observation. (31) • -a flag actual values displayed or not . <enabled>

  16. User Interface::Java Program • // create the engine • ForecastEngine engine = new ForecastEngine(); • // get the input parameters • Properties prop = new Properties(); • prop.load(new FileInputStream(<Expert File>)); • HashMap inputParameters = new HashMap(prop); • inputParameters.put(ParameterConstants.BEGIN,"31"); • inputParameters.put(ParameterConstants.END,"32"); • inputParameters.put(ParameterConstants.INCR,"1"); • inputParameters.put(ParameterConstants.INPUTFILE,“<Time Series File>); • // set the input parameter • engine.setInputParameter(inputParameters

  17. User Interface::Java Program • // execute the experts and obtain the results. • engine.execute(); • // Accessing result : Method 1 • ForecastAnalyzer analyser = engine.getForecastAnalyzer(); • ModelMetrics output[] = analyser.getOptimumForcast(ErrorMeasures.APE, 2); • // Method 2 generate the report • ReportFormat format = new ReportFormat(); • format.setFormat( ReportFormat.FORMAT_BESTK ); • AbstractReportGenerator gen = engine.getDefaultReportGenerator(); • gen.setReportFile("c:/Myoutput.txt"); • gen.generateReport( format );

  18. Adding a new System • Write a XML template file for the scripts for the system. • Modify the module-definition.xml to add the node for the new template file • Write a parameter file to define the forecasting expert • Add the entry for the expertfile in the list of experts property file. • If the output format is different from forecasting system already supported • Create a new OutputExtractor implementing the interface provided. • Write a custom forecast Engine.

  19. Custom ForecastEngine MyForecastEngine extends ForecastEngine{ ForecastEngine(args[]) { Super(); OutputExtractorFactory.add(MyoutputExtractor ob); } public static void main(){ ForecastingEngine = new forecastingEngine(args[]); engine.execute(); }

  20. Time Effort Distribution • Study and Research • Study of basics of Time Series Analysis. • Studying the ETS package in SAS • Studying the Neural network package for MATLAB • Exploring for scripting support for AUTOFIT in SAS • Exploring for inbuilt support for adaptive forecasting • Designing scripts for Adaptive forecasting. • Study of language reference for MATLAB and SAS. • Design and implementation • Design has evolved from prototypical level to sophisticated level. • Prototype was built to test how a system can interact with multiple forecasting systems.

  21. Insights Gained • Familiarity to forecasting systems like SAS and MATLAB • Generic designing using OOP • XML usage for defining small scripting requirements. • Using the best experts from a large ensemble leads to better forecasts. • Adaptive forecasting allows forecasting engine to improve with each new observation. • Analysis of whether some forecasting model performs better for some part of the series may be used to forecast better.

  22. Demo • Best K models • Optimal K models

  23. Demo cont. • Adding a forecasting backend in our application. (JAVA backend) • Write a sample java program • To execute forecasting backend. • Make all parameters “hard-coded” (E.g. input file, output file, other parameter). • Write an xml file (say testforcast.xml) corresponding to this sample java file • Replace all parameters with variables. • Store it in resources/templates/

  24. Demo cont. • Add entry of testforcast.xml file in script.xml file. • Write an expert file (testjava.exp) for this new backend. • Add entry in listofExperts.txt • Make a script • To compile and execute the java script. • Job Done.

  25. Future Enhancement • For user • Incorporating more backend/expert systems to make the forecasting engine more robust. • Use SAS 9 which has more options. • For Developer • Incorporate distributed execution support.

  26. Assumptions & Limitations • Due to generic behavior certain assumptions and restrictions are imposed. • Forecasting system should take the input data in a file. • It should generate the output in the file. • Currently multiple forecasting systems must exist on a single system to use them in combination.

  27. References • Time Series Analysis by Box , Jenkins , Reinsel. • Java Online References • SAS online help • ETS Package • Language Reference • Matlab User Guide • Neural Network toolbox manual • Scripting Language

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