001476087 000__ 03831cam\\22005777i\4500
001476087 001__ 1476087
001476087 003__ OCoLC
001476087 005__ 20231003174632.0
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001476087 008__ 230821s2023\\\\sz\a\\\\ob\\\\000\0\eng\d
001476087 019__ $$a1393241508
001476087 020__ $$a9783031316364$$q(electronic bk.)
001476087 020__ $$a3031316363$$q(electronic bk.)
001476087 020__ $$z9783031316357
001476087 020__ $$z3031316355
001476087 0247_ $$a10.1007/978-3-031-31636-4$$2doi
001476087 035__ $$aSP(OCoLC)1394868484
001476087 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX
001476087 049__ $$aISEA
001476087 050_4 $$aTA342
001476087 08204 $$a620.00151955$$223/eng/20230821
001476087 1001_ $$aMercère, Guillaume,$$eauthor.
001476087 24510 $$aData driven model learning for engineers :$$bwith applications to univariate time series /$$cGuillaume Mercère.
001476087 264_1 $$aCham :$$bSpringer,$$c2023.
001476087 300__ $$a1 online resource (x, 212 pages) :$$billustrations (some color)
001476087 336__ $$atext$$btxt$$2rdacontent
001476087 337__ $$acomputer$$bc$$2rdamedia
001476087 338__ $$aonline resource$$bcr$$2rdacarrier
001476087 504__ $$aIncludes bibliographical references.
001476087 506__ $$aAccess limited to authorized users.
001476087 520__ $$aThe main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate their applicability directly with stationary time series. A multi-step approach is introduced for modeling time series which differs from the mainstream in the literature. Singular spectrum analysis of univariate time series, trend and seasonality modeling with least squares and residual analysis, and modeling with ARMA models are discussed in more detail. As applications of data-driven model learning become widespread in society, engineers need to understand its underlying principles, then the skills to develop and use the resulting data-driven model learning solutions. After reading this book, the users will have acquired the background, the knowledge and confidence to (i) read other model learning textbooks more easily, (ii) use linear algebra and statistics for data analysis and modeling, (iii) explore other fields of applications where model learning from data plays a central role. Thanks to numerous illustrations and simulations, this textbook will appeal to undergraduate and graduate students who need a first course in data-driven model learning. It will also be useful for practitioners, thanks to the introduction of easy-to-implement recipes dedicated to stationary time series model learning. Only a basic familiarity with advanced calculus, linear algebra and statistics is assumed, making the material accessible to students at the advanced undergraduate level.
001476087 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 21, 2023).
001476087 650_0 $$aEngineering$$xMathematical models$$xData processing.
001476087 650_0 $$aTime-series analysis.
001476087 655_0 $$aElectronic books.
001476087 77608 $$iPrint version: $$z3031316355$$z9783031316357$$w(OCoLC)1374089666
001476087 852__ $$bebk
001476087 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-31636-4$$zOnline Access$$91397441.1
001476087 909CO $$ooai:library.usi.edu:1476087$$pGLOBAL_SET
001476087 980__ $$aBIB
001476087 980__ $$aEBOOK
001476087 982__ $$aEbook
001476087 983__ $$aOnline
001476087 994__ $$a92$$bISE