Time series analysis, modeling and applications : a computational intelligence perspective

Temporal 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, Int...

Täydet tiedot

Tallennettuna:
Bibliografiset tiedot
Päätekijä: Pedrycz, Witold, 1953-
Aineistotyyppi: Livre numérique
Kieli:Anglais
Julkaistu: Berlin, Heidelberg : Springer Berlin Heidelberg [20..].
Cham : Springer Nature
Sarja:Intelligent Systems Reference Library 47
Linkit:Accès sur la plateforme de l'éditeur
Accès sur la plateforme de l'éditeur (Springer)
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
Huomautus: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Time Series Analysis, Modeling and Applications, Texte imprimé, 9783642334382
• Time Series Analysis, Modeling and Applications, Texte imprimé, 9783642334405
• Time Series Analysis, Modeling and Applications, Texte imprimé, 9783642437007
Sisällysluettelo:
  • From 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.