LEADER 04275nam 2200601 450 001 9910464482503321 005 20200520144314.0 010 $a87-92982-94-8 035 $a(CKB)3710000000094832 035 $a(EBL)3400139 035 $a(SSID)ssj0001328305 035 $a(PQKBManifestationID)12414905 035 $a(PQKBTitleCode)TC0001328305 035 $a(PQKBWorkID)11283916 035 $a(PQKB)10764318 035 $a(MiAaPQ)EBC3400139 035 $a(Au-PeEL)EBL3400139 035 $a(CaPaEBR)ebr10852715 035 $a(OCoLC)878145315 035 $a(EXLCZ)993710000000094832 100 $a20140408h20132013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aInternet teletraffic modeling and estimation /$fAlexandre Barbosa de Lima and Jose? Roberto de Almeida Amazonas ; cover design by Fernando Freitas 210 1$aAalborg, Denmark :$cRiver Publishers,$d2013. 210 4$dİ2013 215 $a1 online resource (186 p.) 225 0 $aRiver Publishers Series in Information Science and Technology 300 $aDescription based upon print version of record. 311 $a87-92982-10-7 320 $aIncludes bibliographical references and index. 327 $a""Cover""; ""Contents""; ""List of Tables""; ""List of Figures""; ""Preface""; ""List of acronyms and symbols""; ""1 Introduction""; ""1.1 Objectives of telecommunications carriers""; ""1.2 Traffic characteristics""; ""1.3 Questions and contributions""; ""1.4 Time series basic concepts""; ""1.4.1 Time series examples""; ""1.4.2 Operators notation""; ""1.4.3 Stochastic processes""; ""1.4.4 Time seriesmodeling""; ""2 The fractal nature of network traffic""; ""2.1 Fractals and self-similarity examples""; ""2.1.1 The Hurst exponent""; ""2.1.2 Samplemean variance""; ""2.2 Long range dependence"" 327 $a""2.2.1 Aggregate process""""2.3 Self-similarity""; ""2.3.1 Exact second order self-similarity""; ""2.3.2 Impulsiveness""; ""2.4 Final remarks: why is the data networks traffic fractal?""; ""3 Modeling of long-range dependent teletraffic""; ""3.1 Classes of modeling""; ""3.1.1 Non-parametric modeling""; ""3.2 Wavelet transform""; ""3.2.1 Multiresolution analysis and the discrete wavelet transform""; ""3.3 ModelMWM""; ""3.4 Parametric modeling""; ""3.4.1 ARFIMAmodel""; ""3.4.2 ARFIMA models prediction - optimum estimation""; ""3.4.3 Formsof prediction""; ""3.4.4 Confidence interval"" 327 $a""3.4.5 ARFIMAprediction""""3.5 Longmemorystatistical tests""; ""3.5.1 R/Sstatistics""; ""3.5.2 GPHtest""; ""3.6 Some H and d estimation methods""; ""3.6.1 R/Sstatistics""; ""3.6.2 Variance plot""; ""3.6.3 Periodogram method""; ""3.6.4 Whittlea???s method""; ""3.6.5 Haslett and Rafterya???s MV approximate estimator""; ""3.6.6 Abry andVeitcha???swavelet estimator""; ""3.7 Bi-spectrum and linearity test""; ""3.8 KPSS stationarity test""; ""4 State-space modeling""; ""4.1 Introduction""; ""4.2 TARFIMAmodel""; ""4.2.1 Multistep prediction with the Kalman filter"" 327 $a""4.2.2 The prediction power of the TARFIMA model""""4.3 Series exploratory analysis""; ""4.3.1 ARFIMA(0; 0.4; 0) series""; ""4.3.2 MWM series with H = 0.9""; ""4.3.3 Nile river series""; ""4.4 Prediction empirical studywith theTARFIMAmodel""; ""4.4.1 ARFIMA(0, d, 0) series""; ""4.4.2 MWMseries""; ""4.4.3 Nile river series between years 1007 and 1206""; ""4.4.4 Conclusions""; ""5 Modeling of Internet traffic""; ""5.1 Introduction""; ""5.2 Modeling of the UNC02 trace""; ""5.2.1 Exploratory analysis""; ""5.2.2 Long memory local analysis of the UNC02 trace"" 327 $a""5.2.3 Empirical prediction with the TARFIMA model""""6 Conclusions""; ""Bibliography""; ""Index""; ""About the Authors"" 606 $aTelecommunication$xTraffic 608 $aElectronic books. 615 0$aTelecommunication$xTraffic. 676 $a621.3851 700 $ade Lima$b Alexandre Barbosa$0893677 702 $aAmazonas$b Jose? Roberto de Almeida 702 $aFreitas$b Fernando 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910464482503321 996 $aInternet teletraffic modeling and estimation$91996337 997 $aUNINA