Codeless Time Series Analysis with KNIME: 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 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.

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Codeless Time Series Analysis with KNIME: 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 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.

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Codeless Time Series Analysis with KNIME: A practical guide to implementing forecasting models for time series analysis applications

Codeless Time Series Analysis with KNIME: A practical guide to implementing forecasting models for time series analysis applications

Codeless Time Series Analysis with KNIME: A practical guide to implementing forecasting models for time series analysis applications
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Codeless Time Series Analysis with KNIME: A practical guide to implementing forecasting models for time series analysis applications

Codeless Time Series Analysis with KNIME: A practical guide to implementing forecasting models for time series analysis applications

Overview

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.

Product Details

ISBN-13: 9781803239972
Publisher: Packt Publishing
Publication date: 08/19/2022
Sold by: Barnes & Noble
Format: eBook
Pages: 392
File size: 23 MB
Note: This product may take a few minutes to download.

About the Author

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.
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