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This is the code repository for Codeless Time Series Analysis with KNIME, published by Packt.

A practical guide to implementing forecasting models for time series analysis applications

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 book covers the following exciting features:

• 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

If you feel this book is for you, get your copy today!

Following is what you need for this book: 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.

With the following software and hardware list you can run all code files present in the book (Chapter 1-14).

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

• Time Series Analysis with Python Cookbook [Packt] [Amazon]

• Time Series Analysis on AWS [Packt] [Amazon]

Corey Weisinger is a data scientist with KNIME in Austin, Texas. He studied mathematics at Michigan State University focusing on actuarial techniques and functional analysis. Before coming to work for KNIME, he worked as an analytics consultant for the auto industry in Detroit, Michigan. He currently focuses on signal processing and numeric prediction techniques and is the author of the Alteryx to KNIME guidebook.

Maarit Widmann is a data scientist and an educator at KNIME: the instructor behind the KNIME self-paced courses and a teacher in the KNIME courses. She is the author of the From Modeling to Model Evaluation e-book and she publishes regularly in the KNIME blog and on Medium. She holds a Master�s degree in data science and a Bachelor�s degree in sociology.

Daniele Tonini is an experienced advisor and educator in the field of advanced business analytics and machine learning. In the last 15 years, he designed and deployed predictive analytics systems, and data quality management and dynamic reporting tools, mainly for customer intelligence, risk management, and pricing applications. He is an Academic Fellow at Bocconi University (Department of Decision Science) and SDA Bocconi School of Management (Decision Sciences & Business Analytics Faculty). He�s also Adjunct Professor in data mining at Franklin University, Switzerland. He currently teaches statistics, predictive analytics for data-driven decision making, big data and databases, market research, and data mining.

If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.

https://packt.link/free-ebook/9781803232065