Post on 02-Nov-2014
description
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Research Topic
A Study of Data Quality and Analytics
Experimental work
•Predictive Modeling - Linear vs. Nonlinear• GARCH (Generalized Autoregressive
Conditional Heteroskedasticity ) model application on Time series data
• GARCH vs. ANN with Heteroskedasticity • Deployment of Predictive Model using
PMML (Predictive Model Markup Language) synopsis
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Literature Survey
Areas of focus:▪ Predictive Analytics – Various Applications of
Predictive Models ▪ PMML
Resources▪ IEEE Computer Society, Transaction publications▪ International Journal for Research and Application▪ International Institute of Forecasters ▪ ACM Journals /transactions
Status ▪ Literature survey - about 85% completion ▪ Relevant publications extracted : 75+▪ Further survey – Deployment of Model using PMML
Litarature_survey
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Publications
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 of Time 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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Application of R for GARCH
Evaluate GARCH and ARIMA model for forecasting Day ahead electricity demand
Data - Daily Power consumption data Develop Testing Procedure for GARCH
using R programming
Data collection, Data cleaning, Setup the environment, Evaluate Predictive Models , Analysis
paper_1
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GARCH and SARIMA Modeling 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
Data collection, Data cleaning, Setup the environment, Evaluate Predictive Models , Analysis
paper_2
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Neural Networks and SARIMA Modeling 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
Data collection, Data cleaning, Setup the environment, Evaluate Predictive Models , Analysis
paper_3
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Knowledge areas
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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Work plan – Next 6 months
Evaluate the GARCH model for comparing the share price performance of 3 companies
Prototype Development for the Deployment of Predictive model using PMML
WHAT IS ARCH?
Autoregressive Conditional Heteroskedasticity
Predictive (conditional) Uncertainty (heteroskedasticity) That fluctuates over time
(autoregressive)
FROM THE SIMPLE ARCH GREW: GARCH
GENERALIZED ARCH (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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Thank you