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Practical time series analysis : prediction with statistics and machine learning /
Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media,
[2019]
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| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Nielson, Aileen, |e author. | |
| 245 | 1 | 0 | |a Practical time series analysis : |b prediction with statistics and machine learning / |c Aileen Nielson. |
| 250 | |a First edition. | ||
| 264 | 1 | |a Sebastopol, CA : |b O'Reilly Media, |c [2019] | |
| 264 | 4 | |c ©2020 | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from title page (Safari, viewed September 30, 2019). | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Time-series analysis |x Data processing. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a R (Computer program language) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Série chronologique |x Informatique. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 650 | 7 | |a R (Computer program language) |2 fast | |
| 650 | 7 | |a Time-series analysis |x Data processing |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Nielson, Aileen. |t Practical time series analysis. |b First edition. |d Sebastopol, CA : O'Reilly Media, [2019] |w (DLC) 2020301333 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781492041641/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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