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Network Weather Forecasting MAGGIE (NWF). Advisor: Dr Arshad Ali Co-Advisor: Umar kalim Committee Members: Aatif Kamal Kamran hussain. Fareena Saqib BIT-4 A (195) 37fareena@niit.edu.pk fareenas@gmail.com. Problem statement Motivation Project Aim Introduction Scope
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Network Weather ForecastingMAGGIE(NWF) Advisor: Dr Arshad Ali Co-Advisor: Umar kalim Committee Members: Aatif Kamal Kamran hussain Fareena Saqib BIT-4 A (195) 37fareena@niit.edu.pk fareenas@gmail.com
Problem statement Motivation Project Aim Introduction Scope Literature Review Proposed Solution Methodology Project Modules Comparative Analysis Time Line Conclusion Research Accomplishments Future Recommendations Contents
Problem Statement Forecasting the performance of network using technique that better conserves the varying patterns in the data using historical data collected by different active monitoring tools Content
Motivation • GRID Management System • Allocation of task • Parallel processing • Storage Content
Project Aim The aim of project is to develop a module that forecasts the performance of different networks based on historical data. So that efficiency of the system can be increased. Content
Introduction • Forecasting techniques • Holt Winters • ARMA/ARIMA • EWMA • Regression Analysis • Why ARMA/ARIMA? • Varying trends in network data Content
ARMA/ARIMA • Auto Regressive Integrated Moving Average • Box and Jenkins approach • Merger of techniques • Auto Regression (AR) • Moving Average (MA) Benefits of AR Benefits of MA ARIMA Approach Better Results Content
ARMA/ARIMA • Approach followed: • Box and Jenkins approach is followed 1. Identification of the model (Choosing tentative p,d,q) 2. Parameter estimation of the chosen model 3. Forecasting 4. Diagnostic checking (are the estimated residuals white noise?) No (Return to step 1) Content
ARMA/ARIMA • Identification • Through Correlogram • Autocorrelation Function (ACF) • Partial Auto Correlation Function (PACF) Content
ARMA/ARIMA • Estimation • Estimation of order • Estimation of equation • Estimation of coefficients • Forecasting of data • Diagnostic Checking • To check that model is fit to the data. • Obtain residual • Obtain ACF and PACF of residual Content
Use of ARIMA Approach Content
Use of ARMA/ARIMA • Sales of dates contains seasonal effect. • Month of Ramadan • Sales of products • Summer • Winter • Spring • USA economic forecasts • Weather forecasts Content
Network Weather Forecasting • Use of ARIMA in network forecast Network Weather Forecasting Computer Science Field Statistics Network Econometrics Field GRID System Economics Field Content
Scope • Study of different forecasting techniques • Pros and cons • Selection of Technique • Development of methodology • Verification of the algorithm • Modules: • Data Processing module • Forecasting module • Visualization module • Testing Module • Comparative module • Development of user Interface Content
Research Issues • Research Issues: • Development of algorithm using ARIMA approach • Estimation of the coefficients. • Diagnostic Checking tests. Content
Literature Review • Development of algorithm using ARIMA approach • Basic Econometrics by Damodar N.Gujarati • Basic concepts: • Time Series Analysis: • ARMA and ARIMA approach introduction. • Time Series Analysis Forecasting and Control by George E.P Box, Gwilym M.Jenkins,Gregory C.Reinsel: • Study of ARMA/ARIMA in detail. • Box and Jenkins Approach • Basics of statistics • To understand and revise basic concepts of statistics involved in the project. • Research Issues
Literature Review • Estimation of the coefficients. • Estimation of coefficient • http://www.qmw.ac.uk/~ugte133/courses/tseries/8idntify.pdf • Non-linear approaches • http://www.ece.cmu.edu/~moura/papers/icassp88-ribeiro-ieeexplore.pdf • Other approaches • http://www.cs.cmu.edu/afs/cs/project/cmcl/archive/Remulac-papers/tech-report.pdf • Research Issues
Literature Review • Diagnostic Checking tests. • Basic Econometrics by Damodar N.Gujarati • Basic concepts: • Time Series Analysis: • ARMA and ARIMA approach introduction. • Basics of statistics • To understand and revise basic concepts of statistics involved in the project. • Research Issues
Literature Review • Algorithms Involved: • Data Processing • Selection • of Parameter • Trim Operation • Regularization • Algorithm • Moving Average for • Interpolation • Forecasting • Stationarity • Order Estimation • Coefficient • Estimation • Formulation of • equation • Verification • Calculation of • Residuals • Trend Analysis • Portmanteau tests Content
Proposed Solution Content
ARIMA Modeling Postulate General Class of Models Identify Model to be Tentatively Entertained Estimate Parameters in Tentatively Entertained Model Diagnostic Checking yes No Use Model for Forecasting Content
Methodology Content
Methodology Content
Methodology Decision making based on results Content
Network Weather Forecasting Interpolation Regularization Data Trim User Interface Access Data Identification Covariance correlogram Estimation Integration Estimation Of order Estimation of Coefficient Forecasting Diagnostic Checking Content
Architecture Diagram Data files Data Cleaning Docs ……………… Docs ……………… Docs ……………… Estimation GUI User Forecasting Visualization through graphs Content
Use Case Diagram Content
Project Module Content
Data Processing Module Content
Data Cleaning Module • Data files • Define format of the data • Trim operation • Regularization operation • Interpolation operation Content
Data Processing Content
Identification Module Content
Identification Module • Calculated autocorrelations. • Number of lags • Trend analysis of autocorrelation coefficients • Correlogram Content
Identification Module Content
Estimation Module Content
Estimation Module • Estimation Issues: • Random variable generation with normal distribution. • Estimation of the Model • Estimation of the order • Estimation of coefficient • Estimation of the equation • Testing the t-test of the coefficient Content
Order Estimation Content
Forecasting Module Content
Forecasting Module • Processed data with equal time intervals. • Estimation of order. • Formulate Equation. • Estimation of coefficients. • Forecast parameter values. • Plot Graph of forecasted values. Content
Forecasting Content
Residual test Module Content
Residual Module • Graph of the residuals to check white noise • Check if the forecasting is valid or not. • Correlogram of residuals. • Portmanteau tests • To test the Q-values Content
Residual test Content
Visualization Module Content
Visualization Module • Visualization of steps carried by algorithm. • Visualization of the tool developed. • Plots of data at different processing stages. Content
Demo: Snapshot of tool Content
Results Content
Correlogram Content
ARMA/ARIMA results Content