Network Weather Forecasting MAGGIE (NWF)

1. 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

2. Problem statement Motivation Project Aim Introduction Scope Literature Review Proposed Solution Methodology Project Modules Comparative Analysis Time Line Conclusion Research Accomplishments Future Recommendations Contents

3. 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

4. Motivation • GRID Management System • Allocation of task • Parallel processing • Storage Content

5. 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

6. Introduction • Forecasting techniques • Holt Winters • ARMA/ARIMA • EWMA • Regression Analysis • Why ARMA/ARIMA? • Varying trends in network data Content

7. 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

8. 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

9. ARMA/ARIMA • Identification • Through Correlogram • Autocorrelation Function (ACF) • Partial Auto Correlation Function (PACF) Content

10. 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

11. Use of ARIMA Approach Content

12. 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

13. Network Weather Forecasting • Use of ARIMA in network forecast Network Weather Forecasting Computer Science Field Statistics Network Econometrics Field GRID System Economics Field Content

14. 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

15. Research Issues • Research Issues: • Development of algorithm using ARIMA approach • Estimation of the coefficients. • Diagnostic Checking tests. Content

16. 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

17. 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

18. 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

19. 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

20. Proposed Solution Content

21. 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

22. Methodology Content

23. Methodology Content

24. Methodology Decision making based on results Content

25. 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

26. Architecture Diagram Data files Data Cleaning Docs ……………… Docs ……………… Docs ……………… Estimation GUI User Forecasting Visualization through graphs Content

27. Use Case Diagram Content

28. Project Module Content

29. Data Processing Module Content

30. Data Cleaning Module • Data files • Define format of the data • Trim operation • Regularization operation • Interpolation operation Content

31. Data Processing Content

32. Identification Module Content

33. Identification Module • Calculated autocorrelations. • Number of lags • Trend analysis of autocorrelation coefficients • Correlogram Content

34. Identification Module Content

35. Estimation Module Content

36. 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

37. Order Estimation Content

38. Forecasting Module Content

39. 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

40. Forecasting Content

41. Residual test Module Content

42. 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

43. Residual test Content

44. Visualization Module Content

45. Visualization Module • Visualization of steps carried by algorithm. • Visualization of the tool developed. • Plots of data at different processing stages. Content

46. Demo: Snapshot of tool Content

47. Results Content

48. Content

49. Correlogram Content

50. ARMA/ARIMA results Content