Description
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
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
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.
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.
Book Information
ISBN 9781803232065
Author Corey Weisinger
Format Paperback
Page Count 392
Imprint Packt Publishing Limited
Publisher Packt Publishing Limited
