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000945493 019__ $$a1038427235
000945493 020__ $$a9783319949925
000945493 020__ $$a3319949926
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000945493 020__ $$a9783319949918
000945493 0247_ $$a10.1007/978-3-319-94992-5.$$2doi
000945493 035__ $$aSP(OCoLC)on1205459832
000945493 035__ $$aSP(OCoLC)1205459832$$z(OCoLC)1038427235
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000945493 050_4 $$aQ334-342
000945493 050_4 $$aTJ210.2-211.495
000945493 08204 $$a006.3$$223
000945493 1001_ $$aBarba Maggi, Lida Mercedes.,$$eauthor.
000945493 24510 $$aMultiscale Forecasting Models /$$cby Lida Mercedes Barba Maggi.
000945493 264_1 $$aCham :$$bSpringer International Publishing :$$bImprint: Springer,$$c2018.
000945493 300__ $$a1 online resource (XXIV, 124 pages) :$$billustrations.
000945493 336__ $$atext$$btxt$$2rdacontent
000945493 337__ $$acomputer$$bc$$2rdamedia
000945493 338__ $$aonline resource$$bcr$$2rdacarrier
000945493 347__ $$atext file$$bPDF$$2rda
000945493 506__ $$aAccess limited to authorized users.
000945493 520__ $$aThis book presents two new decomposition methods to decompose a time series in intrinsic components of low and high frequencies. The methods are based on Singular Value Decomposition (SVD) of a Hankel matrix (HSVD). The proposed decomposition is used to improve the accuracy of linear and nonlinear auto-regressive models. Linear Auto-regressive models (AR, ARMA and ARIMA) and Auto-regressive Neural Networks (ANNs) have been found insufficient because of the highly complicated nature of some time series. Hybrid models are a recent solution to deal with non-stationary processes which combine pre-processing techniques with conventional forecasters, some pre-processing techniques broadly implemented are Singular Spectrum Analysis (SSA) and Stationary Wavelet Transform (SWT). Although the flexibility of SSA and SWT allows their usage in a wide range of forecast problems, there is a lack of standard methods to select their parameters. The proposed decomposition HSVD and Multilevel SVD are described in detail through time series coming from the transport and fishery sectors. Further, for comparison purposes, it is evaluated the forecast accuracy reached by SSA and SWT, both jointly with AR-based models and ANNs.
000945493 650_0 $$aAlgebra.
000945493 650_0 $$aArtificial intelligence.
000945493 650_0 $$aComputer science.
000945493 650_0 $$aMathematical statistics.
000945493 7730_ $$tSpringer eBooks
000945493 77608 $$iPrint version: $$z9783319949918
000945493 852__ $$bebk
000945493 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-94992-5$$zOnline Access$$91397441.1
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000945493 980__ $$aEBOOK
000945493 980__ $$aBIB
000945493 982__ $$aEbook
000945493 983__ $$aOnline
000945493 994__ $$a92$$bISE