A Study of Data Quality and Analytics
1
Experimental work
• Predictive Modeling - Linear vs. Nonlinear
• GARCH (Generalize...
 Areas of focus:
▪ Predictive Analytics – VariousApplications of Predictive Models
▪ PMML
 Resources
▪ IEEEComputer Soci...
 Application of R Programming for Forecasting Day-ahead
electricity demand - Internal Journal of Computer Science
Issues,...
 Evaluate GARCH and ARIMA model for forecasting
Day ahead electricity demand
 Data - Daily Power consumption data
 Deve...
 Evaluate GARCH and SARIMA model for forecasting
day and night variances in electricity demand
 Data - Hourly Power cons...
 Evaluate SARIMA and Neural Networks model for
forecasting monthly electricity demand
 Data - Monthly Power consumption ...
 Predictive methods and techniques –
▪ Linear Regression – ARMA, ARIMA, SARIMA
▪ Non-linear - Neural Networks, GARCH
 To...
 Evaluate the GARCH model for comparing the
share price performance of 3 companies
 Prototype Development for the Deploy...
 Autoregressive Conditional
Heteroskedasticity
 Predictive (conditional)
 Uncertainty (heteroskedasticity)
 That fluct...
 GENERALIZEDARCH (Bollerslev) a most
important extension
 Tomorrow’s variance is predicted to be a
weighted average of t...
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A study of Data Quality and Analytics

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A study of Data Quality and Analytics

  1. 1. A Study of Data Quality and Analytics 1 Experimental work • Predictive Modeling - Linear vs. Nonlinear • GARCH (Generalized Autoregressive Conditional Heteroskedasticity ) model application onTime series data • GARCH vs. ANN with Heteroskedasticity • Deployment of Predictive Model using PMML (Predictive Model Markup Language)
  2. 2.  Areas of focus: ▪ Predictive Analytics – VariousApplications of Predictive Models ▪ PMML  Resources ▪ IEEEComputer Society,Transaction publications ▪ International Journal for Research andApplication ▪ International Institute of Forecasters ▪ ACM Journals /transactions  Status ▪ Literature survey - about 85% completion ▪ Relevant publications extracted : 75+ ▪ Further survey – Deployment of Model using PMML 2
  3. 3.  Application of R Programming for Forecasting Day-ahead electricity demand - Internal Journal of Computer Science Issues, Vol 9, Issue 6, no 1, Nov 2012  Mining ofTime series data for forecasting Day and Night variances in electricity demand - National Conference on Business Analytics and Business Intelligence , Institute of Public Enterprise , Jan 2013  Forecasting of Electricity Demand using SARIMA and Feed Forward Neural Network Models, Accepted for publication in International Journal of Research in Computer Application and Management 3
  4. 4.  Evaluate GARCH and ARIMA model for forecasting Day ahead electricity demand  Data - Daily Power consumption data  DevelopTesting Procedure for GARCH using R programming 4 Data collection, Data cleaning, Setup the environment, Evaluate Predictive Models ,Analysis
  5. 5.  Evaluate GARCH and SARIMA model for forecasting day and night variances in electricity demand  Data - Hourly Power consumption data  GARCH forecasting has lower RMSE (Root Mean Square Error) than that of SARIMA forecasting 5 Data collection, Data cleaning, Setup the environment, Evaluate Predictive Models , Analysis
  6. 6.  Evaluate SARIMA and Neural Networks model for forecasting monthly electricity demand  Data - Monthly Power consumption data  RMSE of SARIMA fitted model is smaller than that of NN whereas NN forecasting has smaller RMSE (Root Mean Square Error) than that of SARIMA forecasting 6 Data collection, Data cleaning, Setup the environment, Evaluate Predictive Models ,Analysis
  7. 7.  Predictive methods and techniques – ▪ Linear Regression – ARMA, ARIMA, SARIMA ▪ Non-linear - Neural Networks, GARCH  Tools ▪ R Project, IBM SPSS  Data - Power Consumption , Stock exchange data  PMML - Predictive Model Markup Language  Model Deployment using PMML 7
  8. 8.  Evaluate the GARCH model for comparing the share price performance of 3 companies  Prototype Development for the Deployment of Predictive model using PMML 8
  9. 9.  Autoregressive Conditional Heteroskedasticity  Predictive (conditional)  Uncertainty (heteroskedasticity)  That fluctuates over time (autoregressive)
  10. 10.  GENERALIZEDARCH (Bollerslev) a most important extension  Tomorrow’s variance is predicted to be a weighted average of the  Long run average variance  Today’s variance forecast  The news (today’s squared return)
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