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Multiscale Forecasting Models

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Multiscale Forecasting Models/ by Lida Mercedes Barba Maggi.
Author:	Barba Maggi, Lida Mercedes.
Description:	XXIV, 124 p. 91 illus., 89 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	https://doi.org/10.1007/978-3-319-94992-5
ISBN:	9783319949925

Multiscale Forecasting Models
Barba Maggi, Lida Mercedes.

Multiscale Forecasting Models[electronic resource] /by Lida Mercedes Barba Maggi. - 1st ed. 2018. - XXIV, 124 p. 91 illus., 89 illus. in color.online resource.

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.

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.

ISBN: 9783319949925

Standard No.: 10.1007/978-3-319-94992-5doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Multiscale Forecasting Models
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