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Practical Time Series Analysis : Prediction with Statistics and Machine Learning by Aileen Nielsen (2019, Trade Paperback)

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About this product

Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1492041653
ISBN-13
9781492041658
eBay Product ID (ePID)
9038532035

Product Key Features

Number of Pages
497 Pages
Language
English
Publication Name
Practical Time Series Analysis : Prediction with Statistics and Machine Learning
Publication Year
2019
Subject
Data Modeling & Design, Intelligence (Ai) & Semantics, General, Data Processing
Type
Textbook
Subject Area
Mathematics, Computers
Author
Aileen Nielsen
Format
Trade Paperback

Dimensions

Item Height
1 in
Item Weight
29.7 Oz
Item Length
9.1 in
Item Width
7.1 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2020-301333
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
519.55
Synopsis
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. You'll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance, 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 challenges in 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. You'll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance
LC Classification Number
QA280.N495 2019

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Practical Time Series Analysis : Prediction with Statistics and Machine Learning by Aileen Nielsen (2019, Trade Paperback)

About this product

Product Identifiers

Product Key Features

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Additional Product Features

All listings for this product

item 1 Practical Time Series Analysis: Prediction with Statistics and Machine Learning,

item 2 Practical Time Series Analysis: Prediction with Statistics and Machine L - GOOD

item 3 Practical Time Series Analysis: - Paperback, by Nielsen Aileen - Very Good

item 4 Practical Time Series Analysis: Prediction with Statistics and Machine Learning

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Diagnostic and Statistical Manual of Mental Disorders DSM-5-TR by American Psychiatric Association (2022, Trade Paperback)

Counseling the Culturally Diverse : Theory and Practice by David Sue, Derald Wing Sue, Helen A. Neville and Laura Smith (2022, Trade Paperback)

Diagnostic and Statistical Manual of Mental Disorders : DSM-5-TR by American Psychiatric Association (2022, Hardcover)

Research Design : Qualitative, Quantitative, and Mixed Methods Approaches by J. David Creswell and John W. Creswell (2022, Trade Paperback)

Introduction to Algorithms, Fourth Edition by Charles E. Leiserson, Thomas H. Cormen, Ronald L. Rivest and Clifford Stein (2022, Hardcover)

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