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100%: Lida Mercedes Barba Maggi: Multiscale Forecasting Models (ISBN: 9783319949925) 2018, in Englisch, Taschenbuch.Nur diese Ausgabe anzeigen…
100%: Lida Mercedes Barba Maggi: Multiscale Forecasting Models (ISBN: 9783319949918) Springer International Publishing, in Deutsch, Broschiert.Nur diese Ausgabe anzeigen…
100%: Lida Mercedes Barba Maggi: Multiscale Forecasting Models (ISBN: 9783030069506) 2019, in Englisch, Taschenbuch.Nur diese Ausgabe anzeigen…
Multiscale Forecasting Models - 12 Angebote vergleichen
Bester Preis: Fr. 4.96 (€ 5.08)¹ (vom 22.09.2019)1
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Multiscale Forecasting Models (Paperback) (2019)
DE PB NWISBN: 9783030069506 bzw. 3030069508, in Deutsch, Springer, United Kingdom, Taschenbuch, neu.
Von Händler/Antiquariat, The Book Depository EURO [60485773], London, United Kingdom.
Language: English. Brand new 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.
Language: English. Brand new 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.
2
Multiscale Forecasting Models
~EN NW EB DLISBN: 9783319949925 bzw. 3319949926, vermutlich in Englisch, Springer Shop, neu, E-Book, elektronischer Download.
Lieferung aus: Österreich, Lagernd, zzgl. Versandkosten.
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. eBook.
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. eBook.
3
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Multiscale Forecasting Models (2019)
DE NW RPISBN: 9783030069506 bzw. 3030069508, in Deutsch, Springer, neu, Nachdruck.
Von Händler/Antiquariat, Paperbackshop-US [8408184], Wood Dale, IL, U.S.A.
New Book. Shipped from US within 10 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
New Book. Shipped from US within 10 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
4
Multiscale Forecasting Models (2018)
EN NW EB DLISBN: 9783319949925 bzw. 3319949926, in Englisch, Springer, Springer, Springer, neu, E-Book, elektronischer Download.
Lieferung aus: Kanada, in-stock.
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 u.
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 u.
5
Multiscale Forecasting Models
~EN NW EB DLISBN: 9783319949925 bzw. 3319949926, vermutlich in Englisch, Springer International Publishing, neu, E-Book, elektronischer Download.
Lieferung aus: Deutschland, Versandkostenfrei.
Multiscale Forecasting Models: 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. Englisch, Ebook.
Multiscale Forecasting Models: 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. Englisch, Ebook.
6
Symbolbild
Multiscale Forecasting Models (2019)
DE NW RPISBN: 9783030069506 bzw. 3030069508, in Deutsch, Springer, neu, Nachdruck.
Von Händler/Antiquariat, Books2Anywhere [190245], Fairford, GLOS, United Kingdom.
New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
7
Multiscale Forecasting Models
~EN PB NWISBN: 9783319949925 bzw. 3319949926, vermutlich in Englisch, Springer-Verlag GmbH, Taschenbuch, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
8
Multiscale Forecasting Models (2019)
EN PB NWISBN: 9783030069506 bzw. 3030069508, in Englisch, 148 Seiten, Springer, Taschenbuch, neu.
Lieferung aus: Deutschland, Noch nicht erschienen. Versandkostenfrei.
Von Händler/Antiquariat, Amazon.de.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Von Händler/Antiquariat, Amazon.de.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
9
Multiscale Forecasting Models
DE NWISBN: 9783319949918 bzw. 3319949918, in Deutsch, Springer Nature, neu.
Lieferung aus: Vereinigte Staaten von Amerika, Lagernd.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
10
Multiscale Forecasting Models
DE NW EBISBN: 9783319949925 bzw. 3319949926, in Deutsch, Springer Nature, neu, E-Book.
Lieferung aus: Vereinigte Staaten von Amerika, Lagernd.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
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