rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Codeless time series analysis with KNIME
    sub title: a practical guide to implementing forecasting models for time series analysis applications
  name:
    name part: Weisinger, Corey
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Widmann, Maarit
    role:
      role term: author.
  name:
    name part: Tonini, Daniele
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: enk
    place:
      place term: Birmingham
    publisher: Packt Publishing, Limited
    date issued: 2022
    edition: Community edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (392 pages)
  abstract: 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 d…
  note: Corey Weisinger, Maarit Widmann, Daniele Tonini.
  note: Includes bibliographical references.
  subject:
    topic: Data mining
  subject:
    topic: Quantitative research
  subject:
    topic: Open source software
  subject:
    topic: Data mining
  subject:
    topic: Open source software
  subject:
    topic: Quantitative research
  classification: QA76.9.D343
  classification: 005.7/2
  related item:
    title info:
      title: Codeless Time Series Analysis with KNIME
    name:
      name part: Weisinger, Corey.
    origin info:
      publisher: Birmingham : Packt Publishing, Limited, ©2022
  identifier: 9781803239972
  identifier: 1803239972
  identifier: 9781803232065 O'Reilly Media
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record content source: EBLCP
    record creation date: 220820
    record change date: 20260123220554.5
    record identifier: in00004671828
    language of cataloging:
      language term: eng