LEADER 03528nam 22005895 450 001 9910741194703321 005 20251009085027.0 010 $a9783031316364 010 $a3031316363 024 7 $a10.1007/978-3-031-31636-4 035 $a(CKB)27965612600041 035 $a(DE-He213)978-3-031-31636-4 035 $a(PPN)272272647 035 $a(MiAaPQ)EBC30682611 035 $a(Au-PeEL)EBL30682611 035 $a(MiAaPQ)EBC30766880 035 $a(Au-PeEL)EBL30766880 035 $a(EXLCZ)9927965612600041 100 $a20230809d2023 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData Driven Model Learning for Engineers $eWith Applications to Univariate Time Series /$fby Guillaume Mercère 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (X, 212 p. 93 illus., 54 illus. in color.) 311 08$a9783031316357 330 $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. 606 $aTime-series analysis 606 $aMachine learning 606 $aStatistics 606 $aTime Series Analysis 606 $aStatistical Learning 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 615 0$aTime-series analysis. 615 0$aMachine learning. 615 0$aStatistics. 615 14$aTime Series Analysis. 615 24$aStatistical Learning. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 676 $a519.55 676 $a620.00151955 700 $aMercère$b Guillaume$4aut$4http://id.loc.gov/vocabulary/relators/aut$01428817 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910741194703321 996 $aData Driven Model Learning for Engineers$93566199 997 $aUNINA LEADER 00947nam0-22003011i-450 001 990004709990403321 005 20260121121945.0 035 $aFED01000470999 100 $a19990604d1995----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $ay-------001yy 200 1 $aBiblioteca$fApollodoro$gcon il comm. di James G. Frazer$ged. italiana a cura di Giulio Guidorizzi 210 $aMilano$cAdelphi$d1995 215 $aLII, 745 p.$d22 cm 225 1 $aBiblioteca Adelphi$v310 500 10$aBibliotheca$m$919340 610 0 $aMitologia greca 700 1$aApollodorus :$cAtheniensis$f$0167041 702 1$aFrazer,$bJames George$c 702 1$aGuidorizzi,$bGiulio 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990004709990403321 952 $aP2B 610 APOLLOD 01 (1)$bBibl. 19946$fFLFBC 959 $aFLFBC 996 $aBibliotheca$919340 997 $aUNINA