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Data Driven Model Learning for Engineers

106,99 €*

Sofort verfügbar, Lieferzeit: 1-3 Tage

Produktnummer: 1847325107a5634fdeaa8d43f953b6fc24
Autor: Mercère, Guillaume
Themengebiete: Least Squares Model Learning Numerical Optimization Singular Spectrum Analysis Time Series Analysis
Veröffentlichungsdatum: 11.08.2024
EAN: 9783031316388
Sprache: Englisch
Seitenzahl: 212
Produktart: Kartoniert / Broschiert
Verlag: Springer International Publishing
Untertitel: With Applications to Univariate Time Series
Produktinformationen "Data Driven Model Learning for Engineers"
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.

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