LEADER 04173nam 2200637 a 450 001 9910437900803321 005 20200520144314.0 010 $a9781283911436 010 $a1283911434 010 $a9783642334399 010 $a3642334393 024 7 $a10.1007/978-3-642-33439-9 035 $a(CKB)2670000000309173 035 $a(EBL)1082687 035 $a(OCoLC)822028569 035 $a(SSID)ssj0000811976 035 $a(PQKBManifestationID)11463087 035 $a(PQKBTitleCode)TC0000811976 035 $a(PQKBWorkID)10859224 035 $a(PQKB)11459420 035 $a(DE-He213)978-3-642-33439-9 035 $a(MiAaPQ)EBC1082687 035 $a(PPN)168324490 035 $a(EXLCZ)992670000000309173 100 $a20121211d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aTime series analysis, modeling and applications $ea computational intelligence perspective /$fWitold Pedrycz and Shyi-Ming Chen (eds.) 205 $a1st ed. 2013. 210 $aBerlin $cSpringer$d2013 215 $a1 online resource (396 p.) 225 1 $aIntelligent systems reference library,$x1868-4394 ;$v47 300 $aDescription based upon print version of record. 311 08$a9783642437007 311 08$a3642437001 311 08$a9783642334382 311 08$a3642334385 320 $aIncludes bibliographical references and indexes. 327 $aFrom the Contents: The links between statistical and fuzzy models for time series analysis and forecasting -- Incomplete time series: imputation through Genetic Algorithms -- Intelligent aggregation and time series smoothing -- Financial fuzzy Time series models based on ordered fuzzy numbers -- Stochastic-fuzzy knowledge-based approach to temporal data modeling.-A Novel Choquet integral composition forecasting model for time series data based on completed  extensional L-measure -- An application of enhanced knowledge models  to fuzzy time series -- A wavelet transform approach to chaotic short-term forecasting -- Fuzzy forecasting with fractal analysis for the time series of environmental pollution -- Support vector regression with kernel Mahalanobis measure for financial forecast. 330 $aTemporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of application embracing physical and human-generated phenomena (environmental data, financial markets, Internet activities, etc.). A quest for a thorough analysis, interpretation, modeling and prediction of time series comes with an ongoing challenge for developing models that are both accurate and user-friendly (interpretable). The volume is aimed to exploit the conceptual and algorithmic framework of Computational Intelligence (CI) to form a cohesive and comprehensive environment for building models of time series. The contributions covered in the volume are fully reflective of the wealth of the CI technologies by bringing together ideas, algorithms, and numeric studies, which convincingly demonstrate their relevance, maturity and visible usefulness. It reflects upon the truly remarkable diversity of methodological and algorithmic approaches and case studies. This volume is aimed at a broad audience of researchers and practitioners engaged in various branches of operations research, management, social sciences, engineering, and economics. Owing to the nature of the material being covered and a way it has been arranged, it establishes a comprehensive and timely picture of the ongoing pursuits in the area and fosters further developments. 410 0$aIntelligent systems reference library ;$vv. 47. 606 $aTime-series analysis 615 0$aTime-series analysis. 676 $a336.4 701 $aPedrycz$b Witold$f1953-$021029 701 $aChen$b Shyi-Ming$01423979 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437900803321 996 $aTime series analysis, modeling and applications$94199505 997 $aUNINA