LEADER 00000cam a22005177a 4500 
003    OCoLC 
005    20240129213017.0 
006    m     o  d         
007    cr cnu---unuuu 
008    220820s2022    enk     ob    000 0 eng d 
020    9781803239972 
020    1803239972 
035    (OCoLC)1341443844 
037    9781803232065|bO'Reilly Media 
040    EBLCP|beng|epn|cEBLCP|dORMDA|dOCLCQ|dOCLCF|dOCLCQ|dOCLCO 
049    INap 
082 04 005.7/2 
082 04 005.7/2|223/eng/20220823 
099    eBook O'Reilly for Public Libraries 
100 1  Weisinger, Corey,|eauthor. 
245 10 Codeless time series analysis with KNIME :|ba practical 
       guide to implementing forecasting models for time series 
       analysis applications /|cCorey Weisinger, Maarit Widmann, 
       Daniele Tonini.|h[O'Reilly electronic resource] 
250    Community edition. 
260    Birmingham :|bPackt Publishing, Limited,|c2022. 
300    1 online resource (392 pages) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
504    Includes bibliographical references. 
520    Perform time series analysis using KNIME Analytics 
       Platform, covering both statistical methods and machine 
       learning-based methods Key Features Gain a solid 
       understanding of time series analysis and its applications
       using KNIME Learn how to apply popular statistical and 
       machine learning time series analysis techniques Integrate
       other tools such as Spark, H2O, and Keras with KNIME 
       within the same application Book Description This book 
       will take you on a practical journey, teaching you how to 
       implement solutions for many use cases involving time 
       series analysis techniques. This learning journey is 
       organized in a crescendo of difficulty, starting from the 
       easiest yet effective techniques applied to weather 
       forecasting, then introducing ARIMA and its variations, 
       moving on to machine learning for audio signal 
       classification, training deep learning architectures to 
       predict glucose levels and electrical energy demand, and 
       ending with an approach to anomaly detection in IoT. 
       There's no time series analysis book without a solution 
       for stock price predictions and you'll find this use case 
       at the end of the book, together with a few more demand 
       prediction use cases that rely on the integration of KNIME
       Analytics Platform and other external tools. By the end of
       this time series book, you'll have learned about popular 
       time series analysis techniques and algorithms, KNIME 
       Analytics Platform, its time series extension, and how to 
       apply both to common use cases. What you will learn 
       Install and configure KNIME time series integration 
       Implement common preprocessing techniques before analyzing
       data Visualize and display time series data in the form of
       plots and graphs Separate time series data into trends, 
       seasonality, and residuals Train and deploy FFNN and LSTM 
       to perform predictive analysis Use multivariate analysis 
       by enabling GPU training for neural networks Train and 
       deploy an ML-based forecasting model using Spark and H2O 
       Who this book is for This book is for data analysts and 
       data scientists who want to develop forecasting 
       applications on time series data. While no coding skills 
       are required thanks to the codeless implementation of the 
       examples, basic knowledge of KNIME Analytics Platform is 
       assumed. The first part of the book targets beginners in 
       time series analysis, and the subsequent parts of the book
       challenge both beginners as well as advanced users by 
       introducing real-world time series applications. 
588 0  Print version record. 
590    O'Reilly|bO'Reilly Online Learning: Academic/Public 
       Library Edition 
650  0 Data mining. 
650  0 Quantitative research. 
650  0 Open source software. 
650  6 Exploration de données (Informatique) 
650  6 Recherche quantitative. 
650  6 Logiciels libres. 
650  7 Data mining|2fast 
650  7 Open source software|2fast 
650  7 Quantitative research|2fast 
700 1  Widmann, Maarit,|eauthor. 
700 1  Tonini, Daniele,|eauthor. 
776 08 |iPrint version:|aWeisinger, Corey.|tCodeless Time Series 
       Analysis with KNIME.|dBirmingham : Packt Publishing, 
       Limited, ©2022 
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
       learning.oreilly.com/library/view/~/9781803232065/?ar
       |zAvailable on O'Reilly for Public Libraries 
938    ProQuest Ebook Central|bEBLB|nEBL7072635 
994    92|bJFN