1. A Study of Data Quality and Analytics
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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.  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
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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
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4.  Evaluate GARCH and ARIMA model for forecasting
Day ahead electricity demand
 Data - Daily Power consumption data
 DevelopTesting Procedure for GARCH using R
programming
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Data collection, Data cleaning, Setup the environment, Evaluate Predictive Models
,Analysis

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
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Data collection, Data cleaning, Setup the environment, Evaluate Predictive Models ,
Analysis

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
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Data collection, Data cleaning, Setup the environment, Evaluate Predictive Models
,Analysis

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
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8.  Evaluate the GARCH model for comparing the
share price performance of 3 companies
 Prototype Development for the Deployment of
Predictive model using PMML
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9.  Autoregressive Conditional
Heteroskedasticity
 Predictive (conditional)
 Uncertainty (heteroskedasticity)
 That fluctuates over time (autoregressive)

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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