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 ABSTRACTThis thesis deals with forecasting newspaper for a local newspaper business. Currentmethodology is based on classical statistical methods such as Exponential Smoothing.However most of these classical statistical approaches are mainly focused on buildingregression models based only on sample data sets. In the last years new methods based onLearning Machines are being employed for forecasting problems. These methods understanduniversal approximations of nonlinear functions, thus resulting more able to model complexnonlinear phenomena. This study suggest the use of a predictive data mining technique called“Support Vector Regression” for forecasting daily newspaper sales for the major newspaperbusiness in Nicaragua. In order to improve the forecasting accuracy, this thesis quantifies somefactors that affect the forecasts of sales of newspaper such as: promotion, performance peroutlet, weather condition, which are more suitable for this type of product. Our numericalexperiments show that forecasting sales per outlet through SVR and the input variablesconsidered outperform the current methodology employed by the company and other classicalstatistical approaches based on common prediction accuracy measures such as the meanabsolute percentage error “MAPE”. In addition a practical model implemented in Ms Excel3based on genetic algorithm for variable selection and the SVR’s parameter is adapted. Ourresults prove that it is advantageous to apply SVM to forecast daily newspaper demand.
 Table of ContentsABSTRACT ......................................................................................................... 2TABLE OF CONTENTS .................................................................................... 4LIST OF FIGURES ............................................................................................ 6LIST OF TABLES ............................................................................................... 7ACKNOWLEDGMENT .................................................................................... 8CHAPTER 1 INTRODUCTION ....................................................................... 91.1 RESEARCH PROBLEM .................................................................................................. 101.2 RESEARCH OBJECTIVE ................................................................................................ 111.3 RESEARCH METHODOLOGY........................................................................................ 12CHAPTER 2 BACKGROUND THEORY ..................................................... 142.1 FORECASTING METHODS ............................................................................................. 142.1.1 Na#westeur048#ve forecast method ..................................................................................... 152.1.2 Moving average ............................................................................................... 152.1.3 Simple exponential smoothing ......................................................................... 162.1.4 Advanced exponential smoothing .................................................................... 172.2 BASIC IDEA OF SUPPORT VECTOR MACHINE .............................................................. 192.2.1 Empirical Risk Minimization (ERM) .............................................................. 222.2.2 Structural Risk Minimization (SRM)............................................................... 232.3 SUPPORT VECTOR CLASSIFICATION ........................................................................... 252.4 KERNEL FUNCTIONS .................................................................................................... 292.5 SUPPORT VECTOR REGRESSION ................................................................................. 31CHAPTER 3 PARAMETERS OF SVR AND IMPLEMENT ATION ......... 353.1 PARAMETERS OF THE SVR .......................................................................................... 353.1.1 Exhaustive Grid Search.................................................................................... 363.1.2 Cherkassky’s Approach .................................................................................... 363.1.3 A combination of both (Cherkassky’s + Grid search approach) ...................... 373.1.4 Heuristic search (Genetic Algorithms) ............................................................. 3753.2 IMPLEMENTATION ....................................................................................................... 393.2.1 Steps for obtaining the models ......................................................................... 433.2.2 Experiments Results......................................................................................... 44CHAPTER 4 NEWSPAPER FORECASTING CASE OF STUDY ............. 464.1 DATA SET (TRAINING, VALIDATION AND TEST) ............................................................ 464.2 THE SVR MODEL ......................................................................................................... 484.3 RESULTS AND ANALYSIS .............................................................................................. 52CHAPTER 5 CONCLUSION .......................................................................... 625.1 CONTRIBUTIONS .......................................................................................................... 625.2 FUTURE STUDIES .......................................................................................................... 63REFERENCE .................................................................................................... 64APPENDIX ........................................................................................................ 67
 REFERENCEAkbaş, B., Karahoca, D., Karahoca, A., &; G#westeur061#ng#westeur055#r, A. (2014). Predicting newspaper sales byusing data mining techniques. Paper presented at the Proceedings of the 15thInternational Conference on Computer Systems and Technologies.Alon, I., Qi, M., &; Sadowski, R. J. (2001). Forecasting aggregate retail sales:: a comparison ofartificial neural networks and traditional methods. Journal of Retailing and ConsumerServices, 8(3), 147-156. doi: http://dx.doi.org/10.1016/S0969-6989(00)00011-4Bishop, C. M. (2006). Pattern recognition and machine learning (Vol. 1): springer New York.Boser, B. E., Guyon, I. M., &; Vapnik, V. N. (1992). A training algorithm for optimal marginclassifiers. Paper presented at the Proceedings of the fifth annual workshop onComputational learning theory.Box, G. E., Jenkins, G. M., &; Reinsel, G. C. (2013). Time series analysis: forecasting andcontrol: John Wiley &; Sons.Brandimarte, P., &; Zotteri, G. (2007). Introduction to distribution logistics (Vol. 21): JohnWiley &; Sons.Burges, C. J. (1998). A tutorial on support vector machines for pattern recognition. Data miningand knowledge discovery, 2(2), 121-167.Cardoso, G., &; Gomide, F. (2007). Newspaper demand prediction and replacement modelbased on fuzzy clustering and rules. Information Sciences, 177(21), 4799-4809. doi:http://dx.doi.org/10.1016/j.ins.2007.05.009Carter, A. E., &; Ragsdale, C. T. (2002). Scheduling pre-printed newspaper advertising insertsusing genetic algorithms. Omega, 30(6), 415-421. doi:http://dx.doi.org/10.1016/S0305-0483(02)00059-2Chen, K.-Y. (2007). Forecasting systems reliability based on support vector regression withgenetic algorithms. Reliability Engineering &; System Safety, 92(4), 423-432. doi:http://dx.doi.org/10.1016/j.ress.2005.12.014Chen, K.-Y., &; Wang, C.-H. (2007). Support vector regression with genetic algorithms inforecasting tourism demand. Tourism Management, 28(1), 215-226. doi:http://dx.doi.org/10.1016/j.tourman.2005.12.018Cherkassky, V., &; Ma, Y. (2004). Practical selection of SVM parameters and noise estimationfor SVM regression. Neural Networks, 17(1), 113-126. doi:http://dx.doi.org/10.1016/S0893-6080(03)00169-2Chervonenkis., V. V. a. A. (1974). Structural risk minimization. fromhttp://www.svms.org/srm/Chih-Wei Hsu, C.-C. C., and Chih-Jen Lin. ( April 15, 2010). A Practical Guide to SupportVector Classification. Techinical Report.
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