Codeless Time Series Analysis with KNIME
A practical guide to implementing forecasting models for time series analysis applications
Purchase on Amazon
“Codeless Time Series Analysis with KNIME is a perfect storm of codeless data science meets time series analysis meets one of the most popular analytics platforms available.”
Matthew Mayo, KDnuggets
“From basic time series plotting to Fourier Transforms and neural networks, this book covers the topics in an accessible and easy-to-digest manner.”
John Emery, phData
“In general, this book, shifts a beginner to an intermediate or advanced level in time series and KNIME practically.”
Armin Ghassemi Rudd, Data Science instructor and Consultant
“This is a fantastic addition to any data scientist’s technical library.”
Kristi Smith, Cisco
“I especially like Chapter 7, which introduces (S)ARIMA models in the context of temperature forecasting.”
Amazon Reviewer
“The book makes it easy to digest different types of Time Series without having to worry about having to learn how to code.”
Abdul, Amazon Reviewer
Who the book is for
This book is for data analysts and data scientists who want to develop forecasting applications on time series data. Basic knowledge of data transformations is assumed, while no coding skills are required thanks to the codeless implementation of the examples. The first part of the book targets the beginners in time series analysis by introducing the main concepts of time series analysis and visual exploration and preprocessing of time series data. The subsequent parts of the book challenge both beginners and advanced users by introducing real-world time series analysis applications.
What you'll learn
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.
