000 03929nam a22004815i 4500
001 978-3-642-33439-9
003 DE-He213
005 20200421111657.0
007 cr nn 008mamaa
008 121205s2013 gw | s |||| 0|eng d
020 _a9783642334399
_9978-3-642-33439-9
024 7 _a10.1007/978-3-642-33439-9
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aTime Series Analysis, Modeling and Applications
_h[electronic resource] :
_bA Computational Intelligence Perspective /
_cedited by Witold Pedrycz, Shyi-Ming Chen.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aVIII, 404 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v47
505 0 _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.
520 _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.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aPedrycz, Witold.
_eeditor.
700 1 _aChen, Shyi-Ming.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642334382
830 0 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v47
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-33439-9
912 _aZDB-2-ENG
942 _cEBK
999 _c54749
_d54749