03785nam 22006615 450 991074119470332120240628095716.03-031-31636-310.1007/978-3-031-31636-4(CKB)27965612600041(DE-He213)978-3-031-31636-4(PPN)272272647(MiAaPQ)EBC30682611(Au-PeEL)EBL30682611(EXLCZ)992796561260004120230809d2023 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierData Driven Model Learning for Engineers With Applications to Univariate Time Series /by Guillaume Mercère1st ed. 2023.Cham :Springer Nature Switzerland :Imprint: Springer,2023.1 online resource (X, 212 p. 93 illus., 54 illus. in color.)9783031316357 The 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.Time-series analysisMachine learningStatisticsTime Series AnalysisStatistical LearningStatistics in Engineering, Physics, Computer Science, Chemistry and Earth SciencesEnginyeriathubModels matematicsthubProcessament de dadesthubAnàlisi de sèries temporalsthubLlibres electrònicsthubTime-series analysis.Machine learning.Statistics.Time Series Analysis.Statistical Learning.Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.EnginyeriaModels matematicsProcessament de dadesAnàlisi de sèries temporals519.55620.00151955Mercère Guillaumeauthttp://id.loc.gov/vocabulary/relators/aut1428817MiAaPQMiAaPQMiAaPQBOOK9910741194703321Data Driven Model Learning for Engineers3566199UNINA