| 000 | 03645nam a22005415i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-94992-5 | ||
| 003 | DE-He213 | ||
| 005 | 20210201191349.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 180823s2018 gw | s |||| 0|eng d | ||
| 020 |
_a9783319949925 _9978-3-319-94992-5 |
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| 050 | 4 | _aQ334-342 | |
| 072 | 7 |
_aUYQ _2bicssc |
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| 072 | 7 |
_aCOM004000 _2bisacsh |
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| 072 | 7 |
_aUYQ _2thema |
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| 082 | 0 | 4 |
_a006.3 _223 |
| 100 | 1 |
_aBarba Maggi, Lida Mercedes. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 245 | 1 | 0 |
_aMultiscale Forecasting Models _h[electronic resource] / _cby Lida Mercedes Barba Maggi. |
| 250 | _a1st ed. 2018. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2018. |
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| 300 |
_aXXIV, 124 p. 91 illus., 89 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 500 | _aAcceso multiusuario | ||
| 505 | 0 | _aDedication -- 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 | _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. | ||
| 541 |
_fUABC ; _cTemporal ; _d01/01/2021-12/31/2023. |
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| 650 | 0 | _aArtificial intelligence. | |
| 650 | 0 | _aMathematical statistics. | |
| 650 | 0 | _aAlgebra. | |
| 650 | 1 | 4 |
_aArtificial Intelligence. _0https://scigraph.springernature.com/ontologies/product-market-codes/I21000 |
| 650 | 2 | 4 |
_aProbability and Statistics in Computer Science. _0https://scigraph.springernature.com/ontologies/product-market-codes/I17036 |
| 650 | 2 | 4 |
_aAlgebra. _0https://scigraph.springernature.com/ontologies/product-market-codes/M11000 |
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319949918 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319949932 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030069506 |
| 856 | 4 | 0 |
_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-319-94992-5 |
| 912 | _aZDB-2-SCS | ||
| 912 | _aZDB-2-SXCS | ||
| 942 | _cLIBRO_ELEC | ||
| 999 |
_c242610 _d242609 |
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