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Multiscale Forecasting Models

Authors: Barba Maggi, Lida Mercedes

  • The book is unique because it contains two new and competitive methods for time series decomposition to improve the accuracy of auto-regressive models
  • The methods are presented in detail through relevant applications
  • Additionally, the methods are compared with other techniques which are conventionally used in forecasting
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  • ISBN 978-3-319-94992-5
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Hardcover $139.99
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  • ISBN 978-3-319-94991-8
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Softcover $139.99
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  • ISBN 978-3-030-06950-6
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About this book

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.


About the authors

Lida Mercedes Barba Maggi earned a PhD degree in Informatics Engineering from the Pontificia Universidad Católica de Valparaíso, Chile, in 2017. She is currently affiliated with the Universidad Nacional de Chimborazo in Ecuador. Her research interests include Analysis of time series, Forecast and estimate based on mathematical and statistical models, Forecast and estimate based on artificial intelligence, and Optimization Algorithms.

Table of contents (4 chapters)

Table of contents (4 chapters)
  • Times Series Analysis

    Pages 1-29

    Barba Maggi, Lida Mercedes

  • Forecasting Based on Hankel Singular Value Decomposition

    Pages 31-47

    Barba Maggi, Lida Mercedes

  • Multi-Step Ahead Forecasting

    Pages 49-88

    Barba Maggi, Lida Mercedes

  • Multilevel Singular Value Decomposition

    Pages 89-118

    Barba Maggi, Lida Mercedes

Buy this book

eBook $109.00
price for USA in USD
  • ISBN 978-3-319-94992-5
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $139.99
price for USA in USD
  • ISBN 978-3-319-94991-8
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually ready to be dispatched within 3 to 5 business days.
Softcover $139.99
price for USA in USD
  • ISBN 978-3-030-06950-6
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually ready to be dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Multiscale Forecasting Models
Authors
Copyright
2018
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG, part of Springer Nature
eBook ISBN
978-3-319-94992-5
DOI
10.1007/978-3-319-94992-5
Hardcover ISBN
978-3-319-94991-8
Softcover ISBN
978-3-030-06950-6
Edition Number
1
Number of Pages
XXIV, 124
Number of Illustrations
2 b/w illustrations, 89 illustrations in colour
Topics

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