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Data Driven Model Learning for Engineers: With Applications to Univariate Time Series /
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Catalogue Information
Field name
Details
Dewey Class
519.55
Title
Data Driven Model Learning for Engineers (EBook :) : With Applications to Univariate Time Series / / by Guillaume Mercère.
Author
Mercère, Guillaume
Other name(s)
SpringerLink (Online service)
Edition statement
1st ed. 2023.
Publication
Cham : : Springer Nature Switzerland : : Imprint: Springer, , 2023.
Physical Details
X, 212 p. 93 illus., 54 illus. in color. : online resource.
ISBN
9783031316364
Summary Note
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.:
Mode of acces to digital resource
Digital reproduction.-
Cham :
Springer International Publishing,
2023. -
Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format.
System details note
Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
Internet Site
https://doi.org/10.1007/978-3-031-31636-4
Links to Related Works
Subject References:
Machine Learning
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Statistical Learning
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Statistics
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Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
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Time Series Analysis
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Authors:
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Mercère, Guillaume
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Corporate Authors:
SpringerLink (Online service)
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Classification:
519.55
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