國立虎尾科技大學 |

Multiscale forecasting models

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Multiscale forecasting models/ by Lida Mercedes Barba Maggi.
作者:	Maggi, Lida Mercedes Barba.
出版者:	Cham :Springer International Publishing : : 2018.,
面頁冊數:	xxiv, 124 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Time-series analysis. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-94992-5
ISBN:	9783319949925

Multiscale forecasting models
Maggi, Lida Mercedes Barba.

Multiscale forecasting models[electronic resource] /by Lida Mercedes Barba Maggi. - Cham :Springer International Publishing :2018. - xxiv, 124 p. :ill., digital ;24 cm.

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:

528412
Time-series analysis.

LC Class. No.: QA280

Dewey Class. No.: 519.55

Multiscale forecasting models
LDR:02432nam a2200325 a 4500 001 928311
003 DE-He213
005 20180823141935.0
006 m d
007 cr nn 008maaau
008 190626s2018 gw s 0 eng d
020 $a 9783319949925 $q (electronic bk.)
020 $a 9783319949918 $q (paper)
024 7 $a 10.1007/978-3-319-94992-5 $2 doi
035 $a 978-3-319-94992-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA280
072 7 $a UYQ $2 bicssc
072 7 $a TJFM1 $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 519.55 $2 23
090 $a QA280 $b .M193 2018
100 1 $a Maggi, Lida Mercedes Barba. $3 1208230
245 1 0 $a Multiscale forecasting models $h [electronic resource] / $c by Lida Mercedes Barba Maggi.
260 $a Cham : $c 2018. $b Springer International Publishing : $b Imprint: Springer,
300 $a xxiv, 124 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Time-series analysis. $3 528412
650 0 $a Forecasting $x Mathematical models. $3 1135864
650 1 4 $a Computer Science. $3 593922
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 593924
650 2 4 $a Probability and Statistics in Computer Science. $3 669886
650 2 4 $a Algebra. $2 gtt $3 579870
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-94992-5
950 $a Computer Science (Springer-11645)