rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Multiscale Forecasting Models
  name:
    name part: Barba Maggi, Lida Mercedes.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXIV, 124 p. 91 illus., 89 illus. in color. online resource.
  abstract: This book presents two new decomposition methods to decompose a time series in intrinsic components of low and high frequencies. The methods are based on Singular Value Decomposition (SVD) of a Hankel matrix (HSVD). The proposed decomposition is used to improve the accuracy of linear and nonlinear auto-regressive models. Linear Auto-regressive models (AR, ARMA and ARIMA) and Auto-regressive Neural Networks (ANNs) have been found insufficient because of the highly complicated nature of some time series. Hybrid models are a recent solution to deal with non-stationary processes which combine pre-processing techniques with conventional forecasters, some pre-processing techniques broadly implemented are Singular Spectrum Analysis (SSA) and Stationary Wavelet Transform (SWT). Although the flexibility of SSA and SWT allows their usage in a wide range of forecast problems, there is a lack of standard methods to select their parameters. The proposed decomposition HSVD and Multilevel SVD are described in detail through time series coming from the transport and fishery sectors. Further, for comparison purposes, it is evaluated the forecast accuracy reached by SSA and SWT, both jointly with AR-based models and ANNs.
  table of contents: Dedication -- Foreword -- Preface -- Acknowledgement -- List of Tables -- List of Figures -- Acronyms -- 1. Times Series Analysis -- 2. Forecasting based on Hankel Singular Value Decomposition -- 3.Multi-step ahead forecasting -- 4. Multilevel Singular Value Decomposition.
  note: by Lida Mercedes Barba Maggi.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Mathematical statistics
  subject:
    topic: Algebra
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Probability and Statistics in Computer Science
  subject:
    topic: Algebra
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319949925
  identifier: https://doi.org/10.1007/978-3-319-94992-5
  record info:
    record creation date: 180823
    record change date: 20191220125828.0
    record identifier: 978-3-319-94992-5